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Bang, Chang Seok, Hyun Lim, Hae Min Jeong, and Sung Hyeon Hwang. "Use of Endoscopic Images in the Prediction of Submucosal Invasion of Gastric Neoplasms: Automated Deep Learning Model Development and Usability Study." Journal of Medical Internet Research 23, no. 4 (2021): e25167. http://dx.doi.org/10.2196/25167.
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Background In a previous study, we examined the use of deep learning models to classify the invasion depth (mucosa-confined versus submucosa-invaded) of gastric neoplasms using endoscopic images. The external test accuracy reached 77.3%. However, model establishment is labor intense, requiring high performance. Automated deep learning (AutoDL) models, which enable fast searching of optimal neural architectures and hyperparameters without complex coding, have been developed. Objective The objective of this study was to establish AutoDL models to classify the invasion depth of gastric neoplasms. Additionally, endoscopist–artificial intelligence interactions were explored. Methods The same 2899 endoscopic images that were employed to establish the previous model were used. A prospective multicenter validation using 206 and 1597 novel images was conducted. The primary outcome was external test accuracy. Neuro-T, Create ML Image Classifier, and AutoML Vision were used in establishing the models. Three doctors with different levels of endoscopy expertise were asked to classify the invasion depth of gastric neoplasms for each image without AutoDL support, with faulty AutoDL support, and with best performance AutoDL support in sequence. Results The Neuro-T–based model reached 89.3% (95% CI 85.1%-93.5%) external test accuracy. For the model establishment time, Create ML Image Classifier showed the fastest time of 13 minutes while reaching 82.0% (95% CI 76.8%-87.2%) external test accuracy. While the expert endoscopist's decisions were not influenced by AutoDL, the faulty AutoDL misled the endoscopy trainee and the general physician. However, this was corrected by the support of the best performance AutoDL model. The trainee gained the most benefit from the AutoDL support. Conclusions AutoDL is deemed useful for the on-site establishment of customized deep learning models. An inexperienced endoscopist with at least a certain level of expertise can benefit from AutoDL support.
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Chen, Yi-Wei, Qingquan Song, and Xia Hu. "Techniques for Automated Machine Learning." ACM SIGKDD Explorations Newsletter 22, no. 2 (2021): 35–50. http://dx.doi.org/10.1145/3447556.3447567.
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Automated machine learning (AutoML) aims to find optimal machine learning solutions automatically given a problem description, its task type, and datasets. It could release the burden of data scientists from the multifarious manual tuning process and enable the access of domain experts to the off-the-shelf machine learning solutions without extensive experience. In this paper, we portray AutoML as a bi-level optimization problem, where one problem is nested within another to search the optimum in the search space, and review the current developments of AutoML in terms of three categories, automated feature engineering (AutoFE), automated model and hyperparameter tuning (AutoMHT), and automated deep learning (AutoDL). Stateof- the-art techniques in the three categories are presented. The iterative solver is proposed to generalize AutoML techniques. We summarize popular AutoML frameworks and conclude with current open challenges of AutoML.
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Chien, Ching-Syuan. "AutoDL-based convolutional neural networks for wildfire detection." Applied and Computational Engineering 18, no. 1 (2023): 134–42. http://dx.doi.org/10.54254/2755-2721/18/20230978.
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Wildfires have emerged as a pressing issue in many regions of the world due to the ongoing impact of global warming on the planet. However, a reliable and high-performance detection system is currently lacking. This study strives to introduce a wildfire detection system that is based on neural networks and image recognition. This study utilized Edge Impulse to train a neural network to identify wildfire occurrences in the given pictures. To optimize the performance and adaptability of the model, an extensive dataset was compiled by collating images from two Kaggle projects, resulting in a final dataset of over 3000 images. The core technological advancements that Edge Impulse is based on are Auto Deep Learning (AutoDL) and Convolutional Neural Networks (CNN). By applying technologies like Neural Architecture Search (NAS), hyperparameters optimization, and transfer learning, AutoDL enables people interested in machine learning to approach the technology without an extensive understanding of math or programming that machine learning was built upon. CNN is a highly effective and efficient form of neural network popular for image classification. It consists of three different layers: the convolutional layer, the pooling layer, and the fully connected layer. The result of this study consists of a fully functional model for wildfire detection that is ready to be deployed, with a final testing accuracy of over 99%.
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Tuggener, Lukas, Mohammadreza Amirian, Fernando Benites, et al. "Design Patterns for Resource-Constrained Automated Deep-Learning Methods." AI 1, no. 4 (2020): 510–38. http://dx.doi.org/10.3390/ai1040031.
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We present an extensive evaluation of a wide variety of promising design patterns for automated deep-learning (AutoDL) methods, organized according to the problem categories of the 2019 AutoDL challenges, which set the task of optimizing both model accuracy and search efficiency under tight time and computing constraints. We propose structured empirical evaluations as the most promising avenue to obtain design principles for deep-learning systems due to the absence of strong theoretical support. From these evaluations, we distill relevant patterns which give rise to neural network design recommendations. In particular, we establish (a) that very wide fully connected layers learn meaningful features faster; we illustrate (b) how the lack of pretraining in audio processing can be compensated by architecture search; we show (c) that in text processing deep-learning-based methods only pull ahead of traditional methods for short text lengths with less than a thousand characters under tight resource limitations; and lastly we present (d) evidence that in very data- and computing-constrained settings, hyperparameter tuning of more traditional machine-learning methods outperforms deep-learning systems.
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Zimmer, Lucas, Marius Lindauer, and Frank Hutter. "Auto-Pytorch: Multi-Fidelity MetaLearning for Efficient and Robust AutoDL." IEEE Transactions on Pattern Analysis and Machine Intelligence 43, no. 9 (2021): 3079–90. http://dx.doi.org/10.1109/tpami.2021.3067763.
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Liu, Zhengying, Adrien Pavao, Zhen Xu, et al. "Winning Solutions and Post-Challenge Analyses of the ChaLearn AutoDL Challenge 2019." IEEE Transactions on Pattern Analysis and Machine Intelligence 43, no. 9 (2021): 3108–25. http://dx.doi.org/10.1109/tpami.2021.3075372.
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Preuveneers, Davy. "AutoFL: Towards AutoML in a Federated Learning Context." Applied Sciences 13, no. 14 (2023): 8019. http://dx.doi.org/10.3390/app13148019.
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Federated learning (FL) is a decentralized machine learning (ML) technique that learns from distributed data by moving the training process from a centralized server towards many clients rather than centralizing the client data, as is common with classical machine learning. The recent literature on federated learning often focuses on domain-specific use cases (e.g., IoT), investigates various privacy concerns (e.g., membership inference), or analyzes the impact of adversarial attacks (e.g., poisoning) and possible countermeasures. In these works, it is common for the server to have already chosen a specific machine-learning model and predefined hyperparameters prior to initiating the distributed training process. This decision is based on the server’s ability to accomplish the task by either reusing well-established neural network architectures suitable for the specific task (e.g., ResNet-50 for image classification) or evaluating the adequacy of a model using the limited data it has access to. Additionally, the server may also assess publicly available datasets, which may or may not accurately represent real-world data distributions. In this paper, we address the challenge where this step—i.e., the ML model selection and hyperparameter optimization—is not possible in a centralized manner. In such a context, the data of a single client may not be sufficient or not representative enough to construct an ML model configuration that is effective for all clients. In real-world deployments, the data on the different clients may be imbalanced and heterogeneously distributed, and the performance impact of countermeasures is often unclear upfront. While various automated machine learning (AutoML) frameworks have been proposed for classical machine learning and deep learning in a centralized setting, we investigated the practical feasibility of AutoML in a federated learning context while taking into account the presence of security and privacy countermeasures. We implemented and validated our proof-of-concept framework, called AutoFL, on top of open-source libraries for machine learning, federated learning, and hyperparameter optimization, and have demonstrated the added value of our framework with public datasets in different scenarios.
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Wang, Wenbo, and Chengyou Lei. "Training a Minesweeper Agent Using a Convolutional Neural Network." Applied Sciences 15, no. 5 (2025): 2490. https://doi.org/10.3390/app15052490.
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The Minesweeper game is modeled as a sequential decision-making task, for which a neural network architecture, state encoding, and reward function were herein designed. Both a Deep Q-Network (DQN) and supervised learning methods were successfully applied to optimize the training of the game. The experiments were conducted on the AutoDL platform using an NVIDIA RTX 3090 GPU for efficient computation. The results showed that in a 6 × 6 grid with four mines, the DQN model achieved an average win rate of 93.3% (standard deviation: 0.77%), while the supervised learning method achieved 91.2% (standard deviation: 0.9%), both outperforming human players and baseline algorithms and demonstrating high intelligence. The mechanisms of the two methods in the Minesweeper task were analyzed, with the reasons for the faster training speed and more stable performance of supervised learning explained from the perspectives of means–ends analysis and feedback control. Although there is room for improvement in sample efficiency and training stability in the DQN model, its greater generalization ability makes it highly promising for application in more complex decision-making tasks.
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Chen, Xu, and Brett Wujek. "AutoDAL: Distributed Active Learning with Automatic Hyperparameter Selection." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (2020): 3537–44. http://dx.doi.org/10.1609/aaai.v34i04.5759.
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Automated machine learning (AutoML) strives to establish an appropriate machine learning model for any dataset automatically with minimal human intervention. Although extensive research has been conducted on AutoML, most of it has focused on supervised learning. Research of automated semi-supervised learning and active learning algorithms is still limited. Implementation becomes more challenging when the algorithm is designed for a distributed computing environment. With this as motivation, we propose a novel automated learning system for distributed active learning (AutoDAL) to address these challenges. First, automated graph-based semi-supervised learning is conducted by aggregating the proposed cost functions from different compute nodes in a distributed manner. Subsequently, automated active learning is addressed by jointly optimizing hyperparameters in both the classification and query selection stages leveraging the graph loss minimization and entropy regularization. Moreover, we propose an efficient distributed active learning algorithm which is scalable for big data by first partitioning the unlabeled data and replicating the labeled data to different worker nodes in the classification stage, and then aggregating the data in the controller in the query selection stage. The proposed AutoDAL algorithm is applied to multiple benchmark datasets and a real-world electrocardiogram (ECG) dataset for classification. We demonstrate that the proposed AutoDAL algorithm is capable of achieving significantly better performance compared to several state-of-the-art AutoML approaches and active learning algorithms.
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Parker-Holder, Jack, Raghu Rajan, Xingyou Song, et al. "Automated Reinforcement Learning (AutoRL): A Survey and Open Problems." Journal of Artificial Intelligence Research 74 (June 1, 2022): 517–68. http://dx.doi.org/10.1613/jair.1.13596.
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The combination of Reinforcement Learning (RL) with deep learning has led to a series of impressive feats, with many believing (deep) RL provides a path towards generally capable agents. However, the success of RL agents is often highly sensitive to design choices in the training process, which may require tedious and error-prone manual tuning. This makes it challenging to use RL for new problems and also limits its full potential. In many other areas of machine learning, AutoML has shown that it is possible to automate such design choices, and AutoML has also yielded promising initial results when applied to RL. However, Automated Reinforcement Learning (AutoRL) involves not only standard applications of AutoML but also includes additional challenges unique to RL, that naturally produce a different set of methods. As such, AutoRL has been emerging as an important area of research in RL, providing promise in a variety of applications from RNA design to playing games, such as Go. Given the diversity of methods and environments considered in RL, much of the research has been conducted in distinct subfields, ranging from meta-learning to evolution. In this survey, we seek to unify the field of AutoRL, provide a common taxonomy, discuss each area in detail and pose open problems of interest to researchers going forward.
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Patel, Kush. "AutoML and Automated Data Science by Democratizing AI through End-to-End Automation." International Journal for Research in Applied Science and Engineering Technology 12, no. 10 (2024): 494–504. http://dx.doi.org/10.22214/ijraset.2024.64555.
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The rise of data-driven decision-making has led to a significant demand for data science and machine learning (ML) solutions across industries. However, developing these solutions requires extensive expertise in data preprocessing, feature engineering, model selection, hyperparameter tuning and evaluation. AutoML (Automated Machine Learning) and Automated Data Science (AutoDS) have emerged as transformative approaches that aim to democratize data science by automating the endto-end ML pipeline. This paper explores the foundational concepts of AutoML, highlighting key techniques and algorithms, such as neural architecture search (NAS), hyperparameter optimization and meta-learning. We delve into AutoDS's broader scope, which seeks to fully automate tasks from data acquisition to deployment. Real-world applications, such as predictive modeling, anomaly detection and time series forecasting, are examined to demonstrate the impact of these technologies. Additionally, the paper analyzes the current frameworks and platforms facilitating automation, including Auto-sklearn, Google AutoML and H2O.ai and evaluates their performance across different tasks. While the potential to accelerate data science workflows and make AI accessible to non-experts is evident, challenges remain, particularly regarding transparency, interpretability and ethical considerations in fully automated systems. This research provides insights into current trends, future opportunities and the transformative role of AutoML and AutoDS in driving innovation in the data science landscape.
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Cao, Longbing. "Beyond AutoML: Mindful and Actionable AI and AutoAI With Mind and Action." IEEE Intelligent Systems 37, no. 5 (2022): 6–18. http://dx.doi.org/10.1109/mis.2022.3207860.
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Lan, Hai, Yuanjia Zhang, Zhifeng Bao, et al. "AutoDI." Proceedings of the VLDB Endowment 15, no. 12 (2022): 3626–29. http://dx.doi.org/10.14778/3554821.3554860.
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Manual analysis on plan regression is both labor-intensive and inefficient for a large query plan and numerous queries. In this paper, we demonstrate AutoDI, an automatic detection and inference tool that has been developed to investigate why a sub-optimal plan is obtained by analyzing two different plans of the same query. AutoDI consists of two main modules, Difference Finder and Inference. The former aims to find where the two plans are different, and the latter tries to obtain the reasons why the differences come out. In our demonstration, we use a real plan regression in TiDB to show how AutoDI works.
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Yakovlev, Anatoly, Hesam Fathi Moghadam, Ali Moharrer, et al. "Oracle AutoML." Proceedings of the VLDB Endowment 13, no. 12 (2020): 3166–80. http://dx.doi.org/10.14778/3415478.3415542.
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Tornede, Tanja, Alexander Tornede, Jonas Hanselle, Felix Mohr, Marcel Wever, and Eyke Hüllermeier. "Towards Green Automated Machine Learning: Status Quo and Future Directions." Journal of Artificial Intelligence Research 77 (June 12, 2023): 427–57. http://dx.doi.org/10.1613/jair.1.14340.
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Automated machine learning (AutoML) strives for the automatic configuration of machine learning algorithms and their composition into an overall (software) solution — a machine learning pipeline — tailored to the learning task (dataset) at hand. Over the last decade, AutoML has developed into an independent research field with hundreds of contributions. At the same time, AutoML is being criticized for its high resource consumption as many approaches rely on the (costly) evaluation of many machine learning pipelines, as well as the expensive large-scale experiments across many datasets and approaches. In the spirit of recent work on Green AI, this paper proposes Green AutoML, a paradigm to make the whole AutoML process more environmentally friendly. Therefore, we first elaborate on how to quantify the environmental footprint of an AutoML tool. Afterward, different strategies on how to design and benchmark an AutoML tool w.r.t. their “greenness”, i.e., sustainability, are summarized. Finally, we elaborate on how to be transparent about the environmental footprint and what kind of research incentives could direct the community in a more sustainable AutoML research direction. As part of this, we propose a sustainability checklist to be attached to every AutoML paper featuring all core aspects of Green AutoML.
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Thongprayoon, Charat, Pattharawin Pattharanitima, Andrea G. Kattah, et al. "Explainable Preoperative Automated Machine Learning Prediction Model for Cardiac Surgery-Associated Acute Kidney Injury." Journal of Clinical Medicine 11, no. 21 (2022): 6264. http://dx.doi.org/10.3390/jcm11216264.
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Background: We aimed to develop and validate an automated machine learning (autoML) prediction model for cardiac surgery-associated acute kidney injury (CSA-AKI). Methods: Using 69 preoperative variables, we developed several models to predict post-operative AKI in adult patients undergoing cardiac surgery. Models included autoML and non-autoML types, including decision tree (DT), random forest (RF), extreme gradient boosting (XGBoost), and artificial neural network (ANN), as well as a logistic regression prediction model. We then compared model performance using area under the receiver operating characteristic curve (AUROC) and assessed model calibration using Brier score on the independent testing dataset. Results: The incidence of CSA-AKI was 36%. Stacked ensemble autoML had the highest predictive performance among autoML models, and was chosen for comparison with other non-autoML and multivariable logistic regression models. The autoML had the highest AUROC (0.79), followed by RF (0.78), XGBoost (0.77), multivariable logistic regression (0.77), ANN (0.75), and DT (0.64). The autoML had comparable AUROC with RF and outperformed the other models. The autoML was well-calibrated. The Brier score for autoML, RF, DT, XGBoost, ANN, and multivariable logistic regression was 0.18, 0.18, 0.21, 0.19, 0.19, and 0.18, respectively. We applied SHAP and LIME algorithms to our autoML prediction model to extract an explanation of the variables that drive patient-specific predictions of CSA-AKI. Conclusion: We were able to present a preoperative autoML prediction model for CSA-AKI that provided high predictive performance that was comparable to RF and superior to other ML and multivariable logistic regression models. The novel approaches of the proposed explainable preoperative autoML prediction model for CSA-AKI may guide clinicians in advancing individualized medicine plans for patients under cardiac surgery.
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Mustafa, Akram, and Mostafa Rahimi Azghadi. "Automated Machine Learning for Healthcare and Clinical Notes Analysis." Computers 10, no. 2 (2021): 24. http://dx.doi.org/10.3390/computers10020024.
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Machine learning (ML) has been slowly entering every aspect of our lives and its positive impact has been astonishing. To accelerate embedding ML in more applications and incorporating it in real-world scenarios, automated machine learning (AutoML) is emerging. The main purpose of AutoML is to provide seamless integration of ML in various industries, which will facilitate better outcomes in everyday tasks. In healthcare, AutoML has been already applied to easier settings with structured data such as tabular lab data. However, there is still a need for applying AutoML for interpreting medical text, which is being generated at a tremendous rate. For this to happen, a promising method is AutoML for clinical notes analysis, which is an unexplored research area representing a gap in ML research. The main objective of this paper is to fill this gap and provide a comprehensive survey and analytical study towards AutoML for clinical notes. To that end, we first introduce the AutoML technology and review its various tools and techniques. We then survey the literature of AutoML in the healthcare industry and discuss the developments specific to clinical settings, as well as those using general AutoML tools for healthcare applications. With this background, we then discuss challenges of working with clinical notes and highlight the benefits of developing AutoML for medical notes processing. Next, we survey relevant ML research for clinical notes and analyze the literature and the field of AutoML in the healthcare industry. Furthermore, we propose future research directions and shed light on the challenges and opportunities this emerging field holds. With this, we aim to assist the community with the implementation of an AutoML platform for medical notes, which if realized can revolutionize patient outcomes.
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Schlicher, Max, and Klaus Möller. "AutoML im Controlling." Controlling 34, no. 2 (2022): 39–42. http://dx.doi.org/10.15358/0935-0381-2022-2-39.
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Mit der Demokratisierung von künstlicher Intelligenz bekommen auch Controller die Möglichkeit, automatisiertes Maschinelles Lernen einzusetzen. AutoML-Systeme (Automated Machine Learning) können in endnutzer- und expertenorientierte Systeme unterteilt werden. Als Startpunkt für das Controlling eignen sich endnutzerorientierte AutoML-Systeme am besten.
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Zender, Alexander, and Bernhard G. Humm. "Benchmarking Meta AutoML." Procedia Computer Science 256 (2025): 130–41. https://doi.org/10.1016/j.procs.2025.02.105.
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Thirunavukarasu, Arun James, Kabilan Elangovan, Laura Gutierrez, et al. "Clinical performance of automated machine learning: A systematic review." Annals of the Academy of Medicine, Singapore 53, no. 3 - Correct DOI (2024): 187–207. http://dx.doi.org/10.47102/annals-acadmedsg.2023113.
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Introduction: Automated machine learning (autoML) removes technical and technological barriers to building artificial intelligence models. We aimed to summarise the clinical applications of autoML, assess the capabilities of utilised platforms, evaluate the quality of the evidence trialling autoML, and gauge the performance of autoML platforms relative to conventionally developed models, as well as each other. Method: This review adhered to a prospectively registered protocol (PROSPERO identifier CRD42022344427). The Cochrane Library, Embase, MEDLINE and Scopus were searched from inception to 11 July 2022. Two researchers screened abstracts and full texts, extracted data and conducted quality assessment. Disagreement was resolved through discussion and as if required, arbitration by a third researcher. Results: There were 26 distinct autoML platforms featured in 82 studies. Brain and lung disease were the most common fields of study of 22 specialties. AutoML exhibited variable performance: area under the receiver operator characteristic curve (AUCROC) 0.35–1.00, F1-score 0.16–0.99, area under the precision-recall curve (AUPRC) 0.51–1.00. AutoML exhibited the highest AUCROC in 75.6% trials; the highest F1-score in 42.3% trials; and the highest AUPRC in 83.3% trials. In autoML platform comparisons, AutoPrognosis and Amazon Rekognition performed strongest with unstructured and structured data, respectively. Quality of reporting was poor, with a median DECIDE-AI score of 14 of 27. Conclusion: A myriad of autoML platforms have been applied in a variety of clinical contexts. The performance of autoML compares well to bespoke computational and clinical benchmarks. Further work is required to improve the quality of validation studies. AutoML may facilitate a transition to data-centric development, and integration with large language models may enable AI to build itself to fulfil user-defined goals.
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Thirunavukarasu, Arun James, Kabilan Elangovan, Laura Gutierrez, et al. "Clinical performance of automated machine learning: A systematic review." Annals of the Academy of Medicine, Singapore 53, no. 3 (2024): 187–207. http://dx.doi.org/10.47102/https://doi.org/10.47102/annals-acadmedsg.2023113.
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Introduction: Automated machine learning (autoML) removes technical and technological barriers to building artificial intelligence models. We aimed to summarise the clinical applications of autoML, assess the capabilities of utilised platforms, evaluate the quality of the evidence trialling autoML, and gauge the performance of autoML platforms relative to conventionally developed models, as well as each other. Method: This review adhered to a prospectively registered protocol (PROSPERO identifier CRD42022344427). The Cochrane Library, Embase, MEDLINE and Scopus were searched from inception to 11 July 2022. Two researchers screened abstracts and full texts, extracted data and conducted quality assessment. Disagreement was resolved through discussion and as if required, arbitration by a third researcher. Results: There were 26 distinct autoML platforms featured in 82 studies. Brain and lung disease were the most common fields of study of 22 specialties. AutoML exhibited variable performance: area under the receiver operator characteristic curve (AUCROC) 0.35–1.00, F1-score 0.16–0.99, area under the precision-recall curve (AUPRC) 0.51–1.00. AutoML exhibited the highest AUCROC in 75.6% trials; the highest F1-score in 42.3% trials; and the highest AUPRC in 83.3% trials. In autoML platform comparisons, AutoPrognosis and Amazon Rekognition performed strongest with unstructured and structured data, respectively. Quality of reporting was poor, with a median DECIDE-AI score of 14 of 27. Conclusion: A myriad of autoML platforms have been applied in a variety of clinical contexts. The performance of autoML compares well to bespoke computational and clinical benchmarks. Further work is required to improve the quality of validation studies. AutoML may facilitate a transition to data-centric development, and integration with large language models may enable AI to build itself to fulfil user-defined goals.
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Zöller, Marc-André, and Marco F. Huber. "Benchmark and Survey of Automated Machine Learning Frameworks." Journal of Artificial Intelligence Research 70 (January 27, 2021): 409–72. http://dx.doi.org/10.1613/jair.1.11854.
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Machine learning (ML) has become a vital part in many aspects of our daily life. However, building well performing machine learning applications requires highly specialized data scientists and domain experts. Automated machine learning (AutoML) aims to reduce the demand for data scientists by enabling domain experts to build machine learning applications automatically without extensive knowledge of statistics and machine learning. This paper is a combination of a survey on current AutoML methods and a benchmark of popular AutoML frameworks on real data sets. Driven by the selected frameworks for evaluation, we summarize and review important AutoML techniques and methods concerning every step in building an ML pipeline. The selected AutoML frameworks are evaluated on 137 data sets from established AutoML benchmark suites.
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Lazebnik, Teddy, Tzach Fleischer, and Amit Yaniv-Rosenfeld. "Benchmarking Biologically-Inspired Automatic Machine Learning for Economic Tasks." Sustainability 15, no. 14 (2023): 11232. http://dx.doi.org/10.3390/su151411232.
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Data-driven economic tasks have gained significant attention in economics, allowing researchers and policymakers to make better decisions and design efficient policies. Recently, with the advancement of machine learning (ML) and other artificial intelligence (AI) methods, researchers can now solve complex economic tasks with previously unseen performance and ease. However, to use such methods, one is required to have a non-trivial level of expertise in ML or AI, which currently is not standard knowledge in economics. In order to bridge this gap, automatic machine learning (AutoML) models have been developed, allowing non-experts to efficiently use advanced ML models with their data. Nonetheless, not all AutoML models are created equal in general, particularly for the unique properties associated with economic data. In this paper, we present a benchmarking study of biologically inspired and other AutoML techniques for economic tasks. We evaluate four different AutoML models alongside two baseline methods using a set of 50 diverse economic tasks. Our results show that biologically inspired AutoML models (slightly) outperformed non-biological AutoML in economic tasks, while all AutoML models outperformed the traditional methods. Based on our results, we conclude that biologically inspired AutoML has the potential to improve our economic understanding while shifting a large portion of the analysis burden from the economist to a computer.
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Shujaat, Sohaib. "Automated Machine Learning in Dentistry: A Narrative Review of Applications, Challenges, and Future Directions." Diagnostics 15, no. 3 (2025): 273. https://doi.org/10.3390/diagnostics15030273.
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The adoption of automated machine learning (AutoML) in dentistry is transforming clinical practices by enabling clinicians to harness machine learning (ML) models without requiring extensive technical expertise. This narrative review aims to explore the impact of autoML in dental applications. A comprehensive search of PubMed, Scopus, and Google Scholar was conducted without time and language restrictions. Inclusion criteria focused on studies evaluating autoML applications and performance for dental tasks. Exclusion criteria included non-dental studies, single-case reports, and conference abstracts. This review highlights multiple promising applications of autoML in dentistry. Diagnostic tasks showed high accuracy, such as 95.4% precision in dental implant classification and 92% accuracy in paranasal sinus disease detection. Predictive tasks also demonstrated promise, including 84% accuracy for ICU admissions due to dental infections and 93.9% accuracy in orthodontic extraction predictions. AutoML frameworks like Google Vertex AI and H2O AutoML emerged as key tools for these applications. AutoML shows great promise in transforming dentistry by facilitating data-driven decision-making and improving patient care quality through accessible, automated solutions. Future advancements should focus on enhancing model interpretability, developing large and annotated datasets, and creating pipelines tailored to dental tasks. Educating clinicians on autoML and integrating domain-specific knowledge into automated platforms could further bridge the gap between complex ML technology and practical dental applications.
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Tian, Junchi, and Chang Che. "Automated Machine Learning: A Survey of Tools and Techniques." Journal of Industrial Engineering and Applied Science 2, no. 6 (2024): 71–76. https://doi.org/10.70393/6a69656173.323336.
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Automated Machine Learning (AutoML) has revolutionized the field of machine learning by automating complex and time-intensive tasks such as data preprocessing, model selection, and hyperparameter tuning. This study explores the capabilities, limitations, and practical applications of six widely used AutoML tools: Auto-sklearn, TPOT, H2O.ai, Google Cloud AutoML, Microsoft Azure AutoML, and Amazon SageMaker Autopilot. By evaluating these tools across diverse datasets—spanning tabular data, time series, image classification, and text sentiment analysis—the research highlights their predictive performance, computational efficiency, scalability, and explainability. Proprietary tools demonstrated superior scalability and efficiency through cloud integration, while open-source platforms provided more interpretability and flexibility. However, challenges such as lack of transparency in advanced neural architecture search mechanisms and ethical considerations, including bias mitigation, remain prevalent. This study concludes that while AutoML tools significantly lower the barrier to entry for machine learning, ongoing advancements are required to balance performance, usability, and ethical standards, making AutoML an integral solution for real-world applications.
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Rosário, Albérico Travassos, and Anna Carolina Boechat. "How Automated Machine Learning Can Improve Business." Applied Sciences 14, no. 19 (2024): 8749. http://dx.doi.org/10.3390/app14198749.
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Automated Machine Learning (AutoML) is revolutionizing how businesses utilize data, but there seems to be a lack of clarity and a holistic view regarding all its advantages, especially concerning the benefits of AutoML for companies. In order to deeply understand how AutoML can improve businesses, a systematic review examined the bibliometric literature of this field, analyzing 74 academic and scientific documents from the Scopus database. Results showed that AutoML (i) reduces the time and resources needed to develop and deploy machine learning models, (ii) accelerates decision-making and enables quicker responses to market changes, (iii) empowers businesses to build accurate predictive models using sophisticated algorithms, (iv) optimizing model performance for reliable insights and better outcomes, and (v) enhances accessibility by reducing technical barriers and democratizing innovation. As businesses grow, AutoML scales to handle larger datasets and more complex problems without extensive manual intervention. In sum, AutoML enhances efficiency, accuracy, and scalability, becoming a crucial driver of business innovation and success.
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KADIOGLU, Muhammet Ali. "End-to-End AutoML Implementation Framework." Eurasia Proceedings of Science Technology Engineering and Mathematics 19 (December 14, 2022): 35–40. http://dx.doi.org/10.55549/epstem.1218713.
Texte intégralRésumé :
Automated machine learning (AutoML) has been an active research area in recent years. Researchers investigate the potential of AutoML as more stakeholders want to maximize the value of their data. The methods are designed to increase the effectiveness of machine learning (ML), accelerate model development processes, and make it accessible for domain experts that are not ML professionals. The systems without the aid of humans are feasible with AutoML, an area that has been increasingly studied recently. Even though efficiency and automation are two of AutoML's key points, a number of critical steps still require human involvement, such as understanding the characteristics of domain-specific data, defining prediction problems, creating a suitable training dataset, and choosing a promising ML technique. A comprehensive and updated analysis of the state-of-the-art in AutoML is presented in the study. AutoML techniques, including hyperparameter optimization (HPO), feature engineering, and data preparation are presented. As-is prediction structure and AutoML-based benchmark model are compared to show how to implement these methods. It is stated what a real end-to-end machine learning pipeline looks like and which parts of the pipeline have already been automated. Our AutoML implementation framework has been introduced and presented as a road map for the entire ML pipeline. Several unresolved issues with the current AutoML techniques are discussed. The obstacles have been outlined that must be overcome in order to achieve this objective.
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Lazebnik, Teddy, Amit Somech, and Abraham Itzhak Weinberg. "SubStrat." Proceedings of the VLDB Endowment 16, no. 4 (2022): 772–80. http://dx.doi.org/10.14778/3574245.3574261.
Texte intégralRésumé :
Automated machine learning (AutoML) frameworks have become important tools in the data scientist's arsenal, as they dramatically reduce the manual work devoted to the construction of ML pipelines. Such frameworks intelligently search among millions of possible ML pipelines - typically containing feature engineering, model selection, and hyper parameters tuning steps - and finally output an optimal pipeline in terms of predictive accuracy. However, when the dataset is large, each individual configuration takes longer to execute, therefore the overall AutoML running times become increasingly high. To this end, we present SubStrat, an AutoML optimization strategy that tackles the data size, rather than configuration space. It wraps existing AutoML tools, and instead of executing them directly on the entire dataset, SubStrat uses a genetic-based algorithm to find a small yet representative data subset that preserves a particular characteristic of the full data. It then employs the AutoML tool on the small subset, and finally, it refines the resulting pipeline by executing a restricted, much shorter, AutoML process on the large dataset. Our experimental results, performed on three popular AutoML frameworks, Auto-Sklearn, TPOT, and H2O show that SubStrat reduces their running times by 76.3% (on average), with only a 4.15% average decrease in the accuracy of the resulting ML pipeline.
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Liu, Sijia, Parikshit Ram, Deepak Vijaykeerthy, et al. "An ADMM Based Framework for AutoML Pipeline Configuration." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (2020): 4892–99. http://dx.doi.org/10.1609/aaai.v34i04.5926.
Texte intégralRésumé :
We study the AutoML problem of automatically configuring machine learning pipelines by jointly selecting algorithms and their appropriate hyper-parameters for all steps in supervised learning pipelines. This black-box (gradient-free) optimization with mixed integer & continuous variables is a challenging problem. We propose a novel AutoML scheme by leveraging the alternating direction method of multipliers (ADMM). The proposed framework is able to (i) decompose the optimization problem into easier sub-problems that have a reduced number of variables and circumvent the challenge of mixed variable categories, and (ii) incorporate black-box constraints alongside the black-box optimization objective. We empirically evaluate the flexibility (in utilizing existing AutoML techniques), effectiveness (against open source AutoML toolkits), and unique capability (of executing AutoML with practically motivated black-box constraints) of our proposed scheme on a collection of binary classification data sets from UCI ML & OpenML repositories. We observe that on an average our framework provides significant gains in comparison to other AutoML frameworks (Auto-sklearn & TPOT), highlighting the practical advantages of this framework.
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Li, Yang, Yu Shen, Wentao Zhang, et al. "VolcanoML." Proceedings of the VLDB Endowment 14, no. 11 (2021): 2167–76. http://dx.doi.org/10.14778/3476249.3476270.
Texte intégralRésumé :
End-to-end AutoML has attracted intensive interests from both academia and industry, which automatically searches for ML pipelines in a space induced by feature engineering, algorithm/model selection, and hyper-parameter tuning. Existing AutoML systems, however, suffer from scalability issues when applying to application domains with large, high-dimensional search spaces. We present VOLCANOML, a scalable and extensible framework that facilitates systematic exploration of large AutoML search spaces. VOLCANOML introduces and implements basic building blocks that decompose a large search space into smaller ones, and allows users to utilize these building blocks to compose an execution plan for the AutoML problem at hand. VOLCANOML further supports a Volcano-style execution model - akin to the one supported by modern database systems - to execute the plan constructed. Our evaluation demonstrates that, not only does VOLCANOML raise the level of expressiveness for search space decomposition in AutoML, it also leads to actual findings of decomposition strategies that are significantly more efficient than the ones employed by state-of-the-art AutoML systems such as auto-sklearn.
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TOPSAKAL, Oguzhan, and Tahir Cetin AKINCI. "Classification and Regression Using Automatic Machine Learning (AutoML) – Open Source Code for Quick Adaptation and Comparison." Balkan Journal of Electrical and Computer Engineering 11, no. 3 (2023): 257–61. http://dx.doi.org/10.17694/bajece.1312764.
Texte intégralRésumé :
This paper presents a comprehensive exploration of automatic machine learning (AutoML) tools in the context of classification and regression tasks. The focus lies on understanding and illustrating the potential of these tools to accelerate and optimize the process of machine learning, thereby making it more accessible to non-experts. Specifically, we delve into multiple popular open-source AutoML tools and provide illustrative examples of their application. We first discuss the fundamental principles of AutoML, including its key features such as automated data preprocessing, feature engineering, model selection, hyperparameter tuning, and model validation. We subsequently venture into the hands-on application of these tools, demonstrating the implementation of classification and regression tasks using multiple open-source AutoML tools. We provide open-source code samples for two data scenarios for classification and regression, designed to assist readers in quickly adapting AutoML tools for their own projects and in comparing the performance of different tools. We believe that this contribution will aid both practitioners and researchers in harnessing the power of AutoML for efficient and effective machine learning model development.
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Weerts, Hilde, Florian Pfisterer, Matthias Feurer, et al. "Can Fairness be Automated? Guidelines and Opportunities for Fairness-aware AutoML." Journal of Artificial Intelligence Research 79 (February 17, 2024): 639–77. http://dx.doi.org/10.1613/jair.1.14747.
Texte intégralRésumé :
The field of automated machine learning (AutoML) introduces techniques that automate parts of the development of machine learning (ML) systems, accelerating the process and reducing barriers for novices. However, decisions derived from ML models can reproduce, amplify, or even introduce unfairness in our societies, causing harm to (groups of) individuals. In response, researchers have started to propose AutoML systems that jointly optimize fairness and predictive performance to mitigate fairness-related harm. However, fairness is a complex and inherently interdisciplinary subject, and solely posing it as an optimization problem can have adverse side effects. With this work, we aim to raise awareness among developers of AutoML systems about such limitations of fairness-aware AutoML, while also calling attention to the potential of AutoML as a tool for fairness research. We present a comprehensive overview of different ways in which fairness-related harm can arise and the ensuing implications for the design of fairness-aware AutoML. We conclude that while fairness cannot be automated, fairness-aware AutoML can play an important role in the toolbox of ML practitioners. We highlight several open technical challenges for future work in this direction. Additionally, we advocate for the creation of more user-centered assistive systems designed to tackle challenges encountered in fairness work. This article appears in the AI & Society track.
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Castellanos-Nieves, Dagoberto, and Luis García-Forte. "Strategies of Automated Machine Learning for Energy Sustainability in Green Artificial Intelligence." Applied Sciences 14, no. 14 (2024): 6196. http://dx.doi.org/10.3390/app14146196.
Texte intégralRésumé :
Automated machine learning (AutoML) is recognized for its efficiency in facilitating model development due to its ability to perform tasks autonomously, without constant human intervention. AutoML automates the development and optimization of machine learning models, leading to high energy consumption due to the large amount of calculations involved. Hyperparameter optimization algorithms, central to AutoML, can significantly impact its carbon footprint. This work introduces and investigates energy efficiency metrics for advanced hyperparameter optimization algorithms within AutoML. These metrics enable the evaluation and optimization of an algorithm’s energy consumption, considering accuracy, sustainability, and reduced environmental impact. The experimentation demonstrates the application of Green AI principles to AutoML hyperparameter optimization algorithms. It assesses the current sustainability of AutoML practices and proposes strategies to make them more environmentally friendly. The findings indicate a reduction of 28.7% in CO2e emissions when implementing the Green AI strategy, compared to the Red AI strategy. This improvement in sustainability is achieved with a minimal decrease of 0.51% in validation accuracy. This study emphasizes the importance of continuing to investigate sustainability throughout the life cycle of AI, aligning with the three fundamental pillars of sustainable development.
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Helali, Mossad, Essam Mansour, Ibrahim Abdelaziz, Julian Dolby, and Kavitha Srinivas. "A scalable AutoML approach based on graph neural networks." Proceedings of the VLDB Endowment 15, no. 11 (2022): 2428–36. http://dx.doi.org/10.14778/3551793.3551804.
Texte intégralRésumé :
AutoML systems build machine learning models automatically by performing a search over valid data transformations and learners, along with hyper-parameter optimization for each learner. Many AutoML systems use meta-learning to guide search for optimal pipelines. In this work, we present a novel meta-learning system called KGpip which (1) builds a database of datasets and corresponding pipelines by mining thousands of scripts with program analysis, (2) uses dataset embeddings to find similar datasets in the database based on its content instead of metadata-based features, (3) models AutoML pipeline creation as a graph generation problem, to succinctly characterize the diverse pipelines seen for a single dataset. KGpip's meta-learning is a sub-component for AutoML systems. We demonstrate this by integrating KGpip with two AutoML systems. Our comprehensive evaluation using 121 datasets, including those used by the state-of-the-art systems, shows that KGpip significantly outperforms these systems.
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Paladino, Lauren M., Alexander Hughes, Alexander Perera, Oguzhan Topsakal, and Tahir Cetin Akinci. "Evaluating the Performance of Automated Machine Learning (AutoML) Tools for Heart Disease Diagnosis and Prediction." AI 4, no. 4 (2023): 1036–58. http://dx.doi.org/10.3390/ai4040053.
Texte intégralRésumé :
Globally, over 17 million people annually die from cardiovascular diseases, with heart disease being the leading cause of mortality in the United States. The ever-increasing volume of data related to heart disease opens up possibilities for employing machine learning (ML) techniques in diagnosing and predicting heart conditions. While applying ML demands a certain level of computer science expertise—often a barrier for healthcare professionals—automated machine learning (AutoML) tools significantly lower this barrier. They enable users to construct the most effective ML models without in-depth technical knowledge. Despite their potential, there has been a lack of research comparing the performance of different AutoML tools on heart disease data. Addressing this gap, our study evaluates three AutoML tools—PyCaret, AutoGluon, and AutoKeras—against three datasets (Cleveland, Hungarian, and a combined dataset). To evaluate the efficacy of AutoML against conventional machine learning methodologies, we crafted ten machine learning models using the standard practices of exploratory data analysis (EDA), data cleansing, feature engineering, and others, utilizing the sklearn library. Our toolkit included an array of models—logistic regression, support vector machines, decision trees, random forest, and various ensemble models. Employing 5-fold cross-validation, these traditionally developed models demonstrated accuracy rates spanning from 55% to 60%. This performance is markedly inferior to that of AutoML tools, indicating the latter’s superior capability in generating predictive models. Among AutoML tools, AutoGluon emerged as the superior tool, consistently achieving accuracy rates between 78% and 86% across the datasets. PyCaret’s performance varied, with accuracy rates from 65% to 83%, indicating a dependency on the nature of the dataset. AutoKeras showed the most fluctuation in performance, with accuracies ranging from 54% to 83%. Our findings suggest that AutoML tools can simplify the generation of robust ML models that potentially surpass those crafted through traditional ML methodologies. However, we must also consider the limitations of AutoML tools and explore strategies to overcome them. The successful deployment of high-performance ML models designed via AutoML could revolutionize the treatment and prevention of heart disease globally, significantly impacting patient care.
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Jin, Zhengyang. "Exploring the Advancements and Challenges of Automated Machine Learning." Applied and Computational Engineering 8, no. 1 (2023): 732–37. http://dx.doi.org/10.54254/2755-2721/8/20230095.
Texte intégralRésumé :
Automl,a rapidly growing field which is aiming to apply the machine to solve problems that human cant easily deal with . This includes tasks such as feature selection, model selection, and hyperparameter tuning. One of the many advantages about Auto Ml is that it can greatly shorten the cost of researchs and resources cost by applying machine learning to a problem. This makes it accessible to a wider range of users, including those without a background in computer science or statistics.In spite of some advantages of AutoML, many challenges are waiting to be addressed. The main challenge is that it is often challenging to ensure that the models generated by AutoML are of high quality and generalize well to new data. Another challenge is that AutoML can be computationally expensive, which can make it infeasible for some problems. Overall, AutoML has the potential to revolutionize the way we apply machine learning to real-world problems, but it is important to be aware of its limitations and challenges.
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Thirunagalingam, Arunkumar. "Transforming Real-Time Data Processing: The Impact of AutoML on Dynamic Data Pipelines." FMDB Transactions on Sustainable Intelligent Networks 1, no. 2 (2024): 110–19. http://dx.doi.org/10.69888/ftsin.2024.000213.
Texte intégralRésumé :
Modern data-driven applications demand efficient real-time data processing, driving innovation across sectors like e-commerce, banking, and healthcare. However, the manual construction and optimization of data pipelines struggle to meet the challenges of today’s high-velocity, dynamic data environments. Automated machine learning (AutoML) emerges as a transformative technology by automating and enhancing the creation, optimization, and management of data pipelines. This study explores the profound impact of AutoML on dynamic data pipelines, highlighting its role in improving efficiency, adaptability, and scalability in real-time data processing. AutoML streamlines the development process, reducing manual intervention and enabling faster, more accurate decisions. It empowers organizations to adapt swiftly to changing data patterns and business needs, facilitating more responsive and resilient data systems. Additionally, the study delves into the challenges and opportunities of integrating AutoML into real-time data pipelines. Key obstacles, such as ensuring data quality and managing computational resources, are discussed alongside the potential for AutoML to overcome these issues through advanced algorithms and automation. Case studies are presented to demonstrate the practical benefits of AutoML integration, showcasing real-world improvements in pipeline performance and operational efficiency. The findings underline AutoML’s pivotal role in shaping the future of dynamic, real-time data-driven applications.
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Swapna Reddy Anugu. "Democratizing AI: How AutoML is transforming enterprise cloud strategies." World Journal of Advanced Engineering Technology and Sciences 15, no. 1 (2025): 701–8. https://doi.org/10.30574/wjaets.2025.15.1.0275.
Texte intégralRésumé :
Automated Machine Learning (AutoML) is transforming how enterprises develop and implement AI solutions by democratizing access to advanced machine learning capabilities. This paradigm shift enables organizations to overcome traditional barriers to AI adoption by automating complex processes throughout the machine learning lifecycle, from data preprocessing to model deployment and monitoring. By reducing technical complexity and accelerating development cycles, AutoML allows domain experts without specialized data science knowledge to build effective AI solutions that address specific business challenges. Cloud providers have integrated robust AutoML capabilities into their platforms, enabling seamless implementation across various industries, including financial services, manufacturing, and retail. Despite impressive advancements, organizations must remain mindful of limitations regarding specialized applications, model transparency, and data quality requirements as they navigate their AutoML implementation journey.
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Kang, Sungmin, Gabin An, and Shin Yoo. "A Quantitative and Qualitative Evaluation of LLM-Based Explainable Fault Localization." Proceedings of the ACM on Software Engineering 1, FSE (2024): 1424–46. http://dx.doi.org/10.1145/3660771.
Texte intégralRésumé :
Fault Localization (FL), in which a developer seeks to identify which part of the code is malfunctioning and needs to be fixed, is a recurring challenge in debugging. To reduce developer burden, many automated FL techniques have been proposed. However, prior work has noted that existing techniques fail to provide rationales for the suggested locations, hindering developer adoption of these techniques. With this in mind, we propose AutoFL, a Large Language Model (LLM)-based FL technique that generates an explanation of the bug along with a suggested fault location. AutoFL prompts an LLM to use function calls to navigate a repository, so that it can effectively localize faults over a large software repository and overcome the limit of the LLM context length. Extensive experiments on 798 real-world bugs in Java and Python reveal AutoFL improves method-level acc@1 by up to 233.3% over baselines. Furthermore, developers were interviewed on their impression of AutoFL-generated explanations, showing that developers generally liked the natural language explanations of AutoFL, and that they preferred reading a few, high-quality explanations instead of many.
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Yu, Chenyan, Yao Li, Minyue Yin, et al. "Automated Machine Learning in Predicting 30-Day Mortality in Patients with Non-Cholestatic Cirrhosis." Journal of Personalized Medicine 12, no. 11 (2022): 1930. http://dx.doi.org/10.3390/jpm12111930.
Texte intégralRésumé :
Objective: To evaluate the feasibility of automated machine learning (AutoML) in predicting 30-day mortality in non-cholestatic cirrhosis. Methods: A total of 932 cirrhotic patients were included from the First Affiliated Hospital of Soochow University between 2014 and 2020. Participants were divided into training and validation datasets at a ratio of 8.5:1.5. Models were developed on the H2O AutoML platform in the training dataset, and then were evaluated in the validation dataset by area under receiver operating characteristic curves (AUC). The best AutoML model was interpreted by SHapley Additive exPlanation (SHAP) Plot, Partial Dependence Plots (PDP), and Local Interpretable Model Agnostic Explanation (LIME). Results: The model, based on the extreme gradient boosting (XGBoost) algorithm, performed better (AUC 0.888) than the other AutoML models (logistic regression 0.673, gradient boost machine 0.886, random forest 0.866, deep learning 0.830, stacking 0.850), as well as the existing scorings (the model of end-stage liver disease [MELD] score 0.778, MELD-Na score 0.782, and albumin-bilirubin [ALBI] score 0.662). The most key variable in the XGBoost model was high-density lipoprotein cholesterol, followed by creatinine, white blood cell count, international normalized ratio, etc. Conclusion: The AutoML model based on the XGBoost algorithm presented better performance than the existing scoring systems for predicting 30-day mortality in patients with non-cholestatic cirrhosis. It shows the promise of AutoML in its future medical application.
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Bodini, Matteo. "Daily Streamflow Forecasting Using AutoML and Remote-Sensing-Estimated Rainfall Datasets in the Amazon Biomes." Signals 5, no. 4 (2024): 659–89. http://dx.doi.org/10.3390/signals5040037.
Texte intégralRésumé :
Reliable streamflow forecasting is crucial for several tasks related to water-resource management, including planning reservoir operations, power generation via Hydroelectric Power Plants (HPPs), and flood mitigation, thus resulting in relevant social implications. The present study is focused on the application of Automated Machine-Learning (AutoML) models to forecast daily streamflow in the area of the upper Teles Pires River basin, located in the region of the Amazon biomes. The latter area is characterized by extensive water-resource utilization, mostly for power generation through HPPs, and it has a limited hydrological data-monitoring network. Five different AutoML models were employed to forecast the streamflow daily, i.e., auto-sklearn, Tree-based Pipeline Optimization Tool (TPOT), H2O AutoML, AutoKeras, and MLBox. The AutoML input features were set as the time-lagged streamflow and average rainfall data sourced from four rain gauge stations and one streamflow gauge station. To overcome the lack of training data, in addition to the previous features, products estimated via remote sensing were leveraged as training data, including PERSIANN, PERSIANN-CCS, PERSIANN-CDR, and PDIR-Now. The selected AutoML models proved their effectiveness in forecasting the streamflow in the considered basin. In particular, the reliability of streamflow predictions was high both in the case when training data came from rain and streamflow gauge stations and when training data were collected by the four previously mentioned estimated remote-sensing products. Moreover, the selected AutoML models showed promising results in forecasting the streamflow up to a three-day horizon, relying on the two available kinds of input features. As a final result, the present research underscores the potential of employing AutoML models for reliable streamflow forecasting, which can significantly advance water-resource planning and management within the studied geographical area.
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Durmaz Engin, Ceren, Mahmut Ozan Gokkan, Seher Koksaldi, et al. "Comparing Auto-Machine Learning and Expert-Designed Models in Diagnosing Vitreomacular Interface Disorders." Journal of Clinical Medicine 14, no. 8 (2025): 2774. https://doi.org/10.3390/jcm14082774.
Texte intégralRésumé :
Background: The vitreomacular interface (VMI) encompasses a group of retinal disorders that significantly impact vision, requiring accurate classification for effective management. This study aims to compare the effectiveness of an expert-designed custom deep learning (DL) model and a code free Auto Machine Learning (ML) model in classifying optical coherence tomography (OCT) images of VMI disorders. Materials and Methods: A balanced dataset of OCT images across five classes—normal, epiretinal membrane (ERM), idiopathic full-thickness macular hole (FTMH), lamellar macular hole (LMH), and vitreomacular traction (VMT)—was used. The expert-designed model combined ResNet-50 and EfficientNet-B0 architectures with Monte Carlo cross-validation. The AutoML model was created on Google Vertex AI, which handled data processing, model selection, and hyperparameter tuning automatically. Performance was evaluated using average precision, precision, and recall metrics. Results: The expert-designed model achieved an overall balanced accuracy of 95.97% and a Matthews Correlation Coefficient (MCC) of 94.65%. Both models attained 100% precision and recall for normal cases. For FTMH, the expert model reached perfect precision and recall, while the AutoML model scored 97.8% average precision, and 97.4% recall. In VMT detection, the AutoML model showed 99.5% average precision with a slightly lower recall of 94.7% compared to the expert model’s 95%. For ERM, the expert model achieved 95% recall, while the AutoML model had higher precision at 93.9% but a lower recall of 79.5%. In LMH classification, the expert model exhibited 95% precision, compared to 72.3% for the AutoML model, with similar recall for both (88% and 87.2%, respectively). Conclusions: While the AutoML model demonstrated strong performance, the expert-designed model achieved superior accuracy across certain classes. AutoML platforms, although accessible to healthcare professionals, may require further advancements to match the performance of expert-designed models in clinical applications.
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Koh, Joshua C. O., German Spangenberg, and Surya Kant. "Automated Machine Learning for High-Throughput Image-Based Plant Phenotyping." Remote Sensing 13, no. 5 (2021): 858. http://dx.doi.org/10.3390/rs13050858.
Texte intégralRésumé :
Automated machine learning (AutoML) has been heralded as the next wave in artificial intelligence with its promise to deliver high-performance end-to-end machine learning pipelines with minimal effort from the user. However, despite AutoML showing great promise for computer vision tasks, to the best of our knowledge, no study has used AutoML for image-based plant phenotyping. To address this gap in knowledge, we examined the application of AutoML for image-based plant phenotyping using wheat lodging assessment with unmanned aerial vehicle (UAV) imagery as an example. The performance of an open-source AutoML framework, AutoKeras, in image classification and regression tasks was compared to transfer learning using modern convolutional neural network (CNN) architectures. For image classification, which classified plot images as lodged or non-lodged, transfer learning with Xception and DenseNet-201 achieved the best classification accuracy of 93.2%, whereas AutoKeras had a 92.4% accuracy. For image regression, which predicted lodging scores from plot images, transfer learning with DenseNet-201 had the best performance (R2 = 0.8303, root mean-squared error (RMSE) = 9.55, mean absolute error (MAE) = 7.03, mean absolute percentage error (MAPE) = 12.54%), followed closely by AutoKeras (R2 = 0.8273, RMSE = 10.65, MAE = 8.24, MAPE = 13.87%). In both tasks, AutoKeras models had up to 40-fold faster inference times compared to the pretrained CNNs. AutoML has significant potential to enhance plant phenotyping capabilities applicable in crop breeding and precision agriculture.
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Lenkala, Swetha, Revathi Marry, Susmitha Reddy Gopovaram, Tahir Cetin Akinci, and Oguzhan Topsakal. "Comparison of Automated Machine Learning (AutoML) Tools for Epileptic Seizure Detection Using Electroencephalograms (EEG)." Computers 12, no. 10 (2023): 197. http://dx.doi.org/10.3390/computers12100197.
Texte intégralRésumé :
Epilepsy is a neurological disease characterized by recurrent seizures caused by abnormal electrical activity in the brain. One of the methods used to diagnose epilepsy is through electroencephalogram (EEG) analysis. EEG is a non-invasive medical test for quantifying electrical activity in the brain. Applying machine learning (ML) to EEG data for epilepsy diagnosis has the potential to be more accurate and efficient. However, expert knowledge is required to set up the ML model with correct hyperparameters. Automated machine learning (AutoML) tools aim to make ML more accessible to non-experts and automate many ML processes to create a high-performing ML model. This article explores the use of automated machine learning (AutoML) tools for diagnosing epilepsy using electroencephalogram (EEG) data. The study compares the performance of three different AutoML tools, AutoGluon, Auto-Sklearn, and Amazon Sagemaker, on three different datasets from the UC Irvine ML Repository, Bonn EEG time series dataset, and Zenodo. Performance measures used for evaluation include accuracy, F1 score, recall, and precision. The results show that all three AutoML tools were able to generate high-performing ML models for the diagnosis of epilepsy. The generated ML models perform better when the training dataset is larger in size. Amazon Sagemaker and Auto-Sklearn performed better with smaller datasets. This is the first study to compare several AutoML tools and shows that AutoML tools can be utilized to create well-performing solutions for the diagnosis of epilepsy via processing hard-to-analyze EEG timeseries data.
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Bosch, Nigel. "AutoML Feature Engineering for Student Modeling Yields High Accuracy, but Limited Interpretability." Journal of Educational Data Mining 13, no. 2 (2021): 55–79. https://doi.org/10.5281/zenodo.5275315.
Texte intégralRésumé :
Automatic machine learning (AutoML) methods automate the time-consuming, feature-engineering process so that researchers produce accurate student models more quickly and easily. In this paper, we compare two AutoML feature engineering methods in the context of the National Assessment of Educational Progress (NAEP) data mining competition. The methods we compare, Featuretools and TSFRESH (Time Series FeatuRe Extraction on basis of Scalable Hypothesis tests), have rarely been applied in the context of student interaction log data. Thus, we address research questions regarding the accuracy of models built with AutoML features, how AutoML feature types compare to each other and to expert-engineered features, and how interpretable the features are. Additionally, we developed a novel feature selection method that addresses problems applying AutoML feature engineering in this context, where there were many heterogeneous features (over 4,000) and relatively few students. Our entry to the NAEP competition placed 3rd overall on the final held-out dataset and 1st on the public leaderboard, with a final Cohen's kappa = .212 and area under the receiver operating characteristic curve (AUC) = .665 when predicting whether students would manage their time effectively on a math assessment. We found that TSFRESH features were significantly more effective than either Featuretools features or expert-engineered features in this context; however, they were also among the most difficult features to interpret based on a survey of six experts' judgments. Finally, we discuss the tradeoffs between effort and interpretability that arise in AutoML-based student modeling.
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Castellanos-Nieves, Dagoberto, and Luis García-Forte. "Improving Automated Machine-Learning Systems through Green AI." Applied Sciences 13, no. 20 (2023): 11583. http://dx.doi.org/10.3390/app132011583.
Texte intégralRésumé :
Automated machine learning (AutoML), which aims to facilitate the design and optimization of machine-learning models with reduced human effort and expertise, is a research field with significant potential to drive the development of artificial intelligence in science and industry. However, AutoML also poses challenges due to its resource and energy consumption and environmental impact, aspects that have often been overlooked. This paper predominantly centers on the sustainability implications arising from computational processes within the realm of AutoML. Within this study, a proof of concept has been conducted using the widely adopted Scikit-learn library. Energy efficiency metrics have been employed to fine-tune hyperparameters in both Bayesian and random search strategies, with the goal of enhancing the environmental footprint. These findings suggest that AutoML can be rendered more sustainable by thoughtfully considering the energy efficiency of computational processes. The obtained results from the experimentation are promising and align with the framework of Green AI, a paradigm aiming to enhance the ecological footprint of the entire AutoML process. The most suitable proposal for the studied problem, guided by the proposed metrics, has been identified, with potential generalizability to other analogous problems.
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S. Vadar, Dr Parashuram, Dr Tejashree T. Moharekar, and Dr Urmila R. Pol. "COMPARATIVE ANALYSIS OF AUTOMATED MACHINE LEARNING LIBRARIES: PYCARET, H2O, TPOT, AUTO-SKLEARN, AND FLAML." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 11 (2024): 1–8. http://dx.doi.org/10.55041/ijsrem39119.
Texte intégralRésumé :
Automated Machine Learning (AutoML) frameworks have gained significant popularity in recent years, making machine learning accessible to a broader audience by automating many of the tasks traditionally performed by data scientists and domain experts. This paper presents a comparative analysis of five prominent AutoML libraries: PyCaret, H2O.ai, TPOT, Auto-sklearn, and FLAML. Each of these libraries provides automated solutions for model selection, hyperparameter tuning, and other machine learning tasks. The goal of this study is to assess their performance, ease of use, flexibility, and suitability for various types of machine learning problems. The comparison is based on multiple factors, including ease of use, performance, customization, resource efficiency, and suitability. This research aims to help researchers, practitioners, and developers in selecting the most appropriate AutoML library based on their specific needs and resources. Keywords: AutoML, PyCaret, H2O.ai, TPOT, Auto-sklearn, FLAML
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Liam Connor, Rebecca Kelly, Saoirse Murphy, Eoghan McCarthy, and Edward Murray. "Analysis of AutoML Tools in the World of Automated Deep Learning." Fusion of Multidisciplinary Research, An International Journal 5, no. 1 (2024): 541–55. https://doi.org/10.63995/fxpc8243.
Texte intégralRésumé :
In-depth analysis of AutoML (Automated Machine Learning) tools in the context of automated deep learning, a rapidly evolving field aimed at simplifying and optimizing the development of deep learning models. AutoML tools automate various stages of the machine learning pipeline, including data preprocessing, feature selection, model selection, hyperparameter tuning, and model evaluation, making them accessible to non-experts and enhancing productivity for experts. The article reviews prominent AutoML tools, examining their methodologies, strengths, limitations, and performance across different tasks. Through comparative studies and practical applications, the effectiveness of these tools in producing robust and efficient deep learning models is evaluated. Additionally, the article explores emerging trends and future directions in the integration of AutoML with deep learning, highlighting the potential for these tools to revolutionize machine learning workflows and democratize access to advanced AI technologies.
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Casapu, Cristina-Ioana, and Simona Moldovanu. "Classification of Microorganism Using Convolutional Neural Network and H2O AutoML." SYSTEM THEORY, CONTROL AND COMPUTING JOURNAL 4, no. 1 (2024): 15–21. http://dx.doi.org/10.52846/stccj.2024.4.1.60.
Texte intégralRésumé :
The study advances microorganism image classification through a hybrid approach that integrates a Convolutional Neural Network (CNN), modified from the VGG19 architecture, with an ensemble model powered by H2O AutoML. Employing data augmentation and feature extraction, the approach enhances performance on a dataset encapsulating a broad spectrum of microorganism classes. The CNN model shows significant accuracy enhancements in complex bacteria classes, as depicted by the confusion matrix. Concurrently, the AutoML ensemble delivers comparable accuracy, notably in some classes where CNNs struggles. This research highlights the complementary strengths of deep learning and AutoML, demonstrating their impact in achieving high-precision microorganism recognition. Such advancements promise to significantly benefit bioinformatics and diagnostic applications, addressing the complexity of multi-class image classification tasks. The results indicate a successful combination of CNN and AutoML methodologies, setting a benchmark in automated microorganism classification, and also showcase the unique contributions and nuances of each method.
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Singpai, Bodin, and Desheng Wu. "Using a DEA–AutoML Approach to Track SDG Achievements." Sustainability 12, no. 23 (2020): 10124. http://dx.doi.org/10.3390/su122310124.
Texte intégralRésumé :
Each country needs to monitor progress on their Sustainable Development Goals (SDGs) to develop strategies that meet the expectations of the United Nations. Data envelope analysis (DEA) can help identify best practices for SDGs by setting goals to compete against. Automated machine learning (AutoML) simplifies machine learning for researchers who need less time and manpower to predict future situations. This work introduces an integrative method that integrates DEA and AutoML to assess and predict performance in SDGs. There are two experiments with different data properties in their interval and correlation to demonstrate the approach. Three prediction targets are set to measure performance in the regression, classification, and multi-target regression algorithms. The back-propagation neural network (BPNN) is used to validate the outputs of the AutoML. As a result, AutoML can outperform BPNN for regression and classification prediction problems. Low standard deviation (SD) data result in poor prediction performance for the BPNN, but does not have a significant impact on AutoML. Highly correlated data result in a higher accuracy, but does not significantly affect the R-squared values between the actual and predicted values. This integrative approach can accurately predict the projected outputs, which can be used as national goals to transform an inefficient country into an efficient country.