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Journal articles on the topic 'Hyperparameter selection and optimization'

Author: Grafiati

Published: 25 May 2024

Last updated: 19 July 2025

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1

Sun, Yunlei, Huiquan Gong, Yucong Li, and Dalin Zhang. "Hyperparameter Importance Analysis based on N-RReliefF Algorithm." International Journal of Computers Communications & Control 14, no. 4 (2019): 557–73. http://dx.doi.org/10.15837/ijccc.2019.4.3593.

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Hyperparameter selection has always been the key to machine learning. The Bayesian optimization algorithm has recently achieved great success, but it has certain constraints and limitations in selecting hyperparameters. In response to these constraints and limitations, this paper proposed the N-RReliefF algorithm, which can evaluate the importance of hyperparameters and the importance weights between hyperparameters. The N-RReliefF algorithm estimates the contribution of a single hyperparameter to the performance according to the influence degree of each hyperparameter on the performance and c

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Bengio, Yoshua. "Gradient-Based Optimization of Hyperparameters." Neural Computation 12, no. 8 (2000): 1889–900. http://dx.doi.org/10.1162/089976600300015187.

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Many machine learning algorithms can be formulated as the minimization of a training criterion that involves a hyperparameter. This hyperparameter is usually chosen by trial and error with a model selection criterion. In this article we present a methodology to optimize several hyper-parameters, based on the computation of the gradient of a model selection criterion with respect to the hyperparameters. In the case of a quadratic training criterion, the gradient of the selection criterion with respect to the hyperparameters is efficiently computed by backpropagating through a Cholesky decomposi

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Nystrup, Peter, Erik Lindström, and Henrik Madsen. "Hyperparameter Optimization for Portfolio Selection." Journal of Financial Data Science 2, no. 3 (2020): 40–54. http://dx.doi.org/10.3905/jfds.2020.1.035.

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Li, Yang, Jiawei Jiang, Jinyang Gao, Yingxia Shao, Ce Zhang, and Bin Cui. "Efficient Automatic CASH via Rising Bandits." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (2020): 4763–71. http://dx.doi.org/10.1609/aaai.v34i04.5910.

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The Combined Algorithm Selection and Hyperparameter optimization (CASH) is one of the most fundamental problems in Automatic Machine Learning (AutoML). The existing Bayesian optimization (BO) based solutions turn the CASH problem into a Hyperparameter Optimization (HPO) problem by combining the hyperparameters of all machine learning (ML) algorithms, and use BO methods to solve it. As a result, these methods suffer from the low-efficiency problem due to the huge hyperparameter space in CASH. To alleviate this issue, we propose the alternating optimization framework, where the HPO problem for e

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Li, Yuqi. "Discrete Hyperparameter Optimization Model Based on Skewed Distribution." Mathematical Problems in Engineering 2022 (August 9, 2022): 1–10. http://dx.doi.org/10.1155/2022/2835596.

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As for the machine learning algorithm, one of the main factors restricting its further large-scale application is the value of hyperparameter. Therefore, researchers have done a lot of original numerical optimization algorithms to ensure the validity of hyperparameter selection. Based on previous studies, this study innovatively puts forward a model generated using skewed distribution (gamma distribution) as hyperparameter fitting and combines the Bayesian estimation method and Gauss hypergeometric function to propose a mathematically optimal solution for discrete hyperparameter selection. The

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Mohapatra, Shubhankar, Sajin Sasy, Xi He, Gautam Kamath, and Om Thakkar. "The Role of Adaptive Optimizers for Honest Private Hyperparameter Selection." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 7 (2022): 7806–13. http://dx.doi.org/10.1609/aaai.v36i7.20749.

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Hyperparameter optimization is a ubiquitous challenge in machine learning, and the performance of a trained model depends crucially upon their effective selection. While a rich set of tools exist for this purpose, there are currently no practical hyperparameter selection methods under the constraint of differential privacy (DP). We study honest hyperparameter selection for differentially private machine learning, in which the process of hyperparameter tuning is accounted for in the overall privacy budget. To this end, we i) show that standard composition tools outperform more advanced techniqu

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ZLOBIN, Mykola, and Volodymyr BAZYLEVYCH. "BAYESIAN OPTIMIZATION FOR TUNING HYPERPARAMETRS OF MACHINE LEARNING MODELS: A PERFORMANCE ANALYSIS IN XGBOOST." Computer systems and information technologies, no. 1 (March 27, 2025): 141–46. https://doi.org/10.31891/csit-2025-1-16.

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The performance of machine learning models depends on the selection and tuning of hyperparameters. As a widely used gradient boosting method, XGBoost relies on optimal hyperparameter configurations to balance model complexity, prevent overfitting, and improve generalization. Especially in high-dimensional hyperparameter spaces, traditional approaches including grid search and random search are computationally costly and ineffective. Recent findings in automated hyperparameter tuning, specifically Bayesian optimization with the tree-structured parzen estimator have shown promise in raising the

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Hafidi, Nasreddine, Zakaria Khoudi, Mourad Nachaoui, and Soufiane Lyaqini. "Cryptocurrency Price Prediction with Genetic Algorithm-based Hyperparameter Optimization." Statistics, Optimization & Information Computing 13, no. 5 (2025): 1947–71. https://doi.org/10.19139/soic-2310-5070-2035.

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Accurate cryptocurrency price forecasting is crucial for investors and researchers in the dynamic and unpredictable cryptocurrency market. Existing models face challenges in incorporating various cryptocurrencies and determining the most effective hyperparameters, leading to reduced forecast accuracy. This study introduces an innovative approach that automates hyperparameter selection, improving accuracy by uncovering complex interconnections among cryptocurrencies. Our methodology leverages deep learning techniques, particularly Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LST

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Kurnia, Deni, Muhammad Itqan Mazdadi, Dwi Kartini, Radityo Adi Nugroho, and Friska Abadi. "Seleksi Fitur dengan Particle Swarm Optimization pada Klasifikasi Penyakit Parkinson Menggunakan XGBoost." Jurnal Teknologi Informasi dan Ilmu Komputer 10, no. 5 (2023): 1083–94. http://dx.doi.org/10.25126/jtiik.20231057252.

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Penyakit Parkinson merupakan gangguan pada sistem saraf pusat yang mempengaruhi sistem motorik. Diagnosis penyakit ini cukup sulit dilakukan karena gejalanya yang serupa dengan penyakit lain. Saat ini diagnosa dapat dilakukan menggunakan machine learning dengan memanfaatkan rekaman suara pasien. Fitur yang dihasilkan dari ekstraksi rekaman suara tersebut relatif cukup banyak sehingga seleksi fitur perlu dilakukan untuk menghindari memburuknya kinerja sebuah model. Pada penelitian ini, Particle Swarm Optimization digunakan sebagai seleksi fitur, sedangkan XGBoost akan digunakan sebagai model kl

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Kurnia, Deni, Muhammad Itqan Mazdadi, Dwi Kartini, Radityo Adi Nugroho, and Friska Abadi. "Seleksi Fitur dengan Particle Swarm Optimization pada Klasifikasi Penyakit Parkinson Menggunakan XGBoost." Jurnal Teknologi Informasi dan Ilmu Komputer 10, no. 5 (2023): 1083–94. https://doi.org/10.25126/jtiik.2023107252.

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Penyakit Parkinson merupakan gangguan pada sistem saraf pusat yang mempengaruhi sistem motorik. Diagnosis penyakit ini cukup sulit dilakukan karena gejalanya yang serupa dengan penyakit lain. Saat ini diagnosa dapat dilakukan menggunakan machine learning dengan memanfaatkan rekaman suara pasien. Fitur yang dihasilkan dari ekstraksi rekaman suara tersebut relatif cukup banyak sehingga seleksi fitur perlu dilakukan untuk menghindari memburuknya kinerja sebuah model. Pada penelitian ini, Particle Swarm Optimization digunakan sebagai seleksi fitur, sedangkan XGBoost akan digunakan sebagai model kl

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Kadek Eka Sapta Wijaya, Gede Angga Pradipta, and Dadang Hermawan. "The Implementation of Bayesian Optimization for Automatic Parameter Selection in Convolutional Neural Network for Lung Nodule Classification." Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) 13, no. 3 (2024): 438–49. https://doi.org/10.23887/janapati.v13i3.82467.

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Lung cancer's high mortality rate makes early detection crucial. Machine learning techniques, especially convolutional neural networks (CNN), play a very important role in lung nodule detection. Traditional machine learning approaches often require manual feature extraction, while CNNs, as a specialized type of neural network, automatically learn features directly from the data. However, tuning CNN hyperparameters, such as network structure and training parameters, is computationally intensive. Bayesian Optimization offers a solution by efficiently selecting parameter values. This study develo

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A Ilemobayo, Justus, Olamide Durodola, Oreoluwa Alade, et al. "Hyperparameter Tuning in Machine Learning: A Comprehensive Review." Journal of Engineering Research and Reports 26, no. 6 (2024): 388–95. http://dx.doi.org/10.9734/jerr/2024/v26i61188.

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Hyperparameter tuning is essential for optimizing the performance and generalization of machine learning (ML) models. This review explores the critical role of hyperparameter tuning in ML, detailing its importance, applications, and various optimization techniques. Key factors influencing ML performance, such as data quality, algorithm selection, and model complexity, are discussed, along with the impact of hyperparameters like learning rate and batch size on model training. Various tuning methods are examined, including grid search, random search, Bayesian optimization, and meta-learning. Spe

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Lopes, Jessica Fernandes, Sylvio Barbon Junior, and Leonimer Flávio de Melo. "Online Meta-Recommendation of CUSUM Hyperparameters for Enhanced Drift Detection." Sensors 25, no. 9 (2025): 2787. https://doi.org/10.3390/s25092787.

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With the increasing demand for time-series analysis, driven by the proliferation of IoT devices and real-time data-driven systems, detecting change points in time series has become critical for accurate short-term prediction. The variability in patterns necessitates frequent analysis to sustain high performance by acquiring the hyperparameter. The Cumulative Sum (CUSUM) method, based on calculating the cumulative values within a time series, is commonly used for change detection due to its early detection of small drifts, simplicity, low computational cost, and robustness to noise. However, it

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Prochukhan, Dmytro. "IMPLEMENTATION OF TECHNOLOGY FOR IMPROVING THE QUALITY OF SEGMENTATION OF MEDICAL IMAGES BY SOFTWARE ADJUSTMENT OF CONVOLUTIONAL NEURAL NETWORK HYPERPARAMETERS." Information and Telecommunication Sciences, no. 1 (June 24, 2023): 59–63. http://dx.doi.org/10.20535/2411-2976.12023.59-63.

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Background. The scientists have built effective convolutional neural networks in their research, but the issue of optimal setting of the hyperparameters of these neural networks remains insufficiently researched. Hyperparameters affect model selection. They have the greatest impact on the number and size of hidden layers. Effective selection of hyperparameters improves the speed and quality of the learning algorithm. It is also necessary to pay attention to the fact that the hyperparameters of the convolutional neural network are interconnected. That is why it is very difficult to manually sel

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Raji, Ismail Damilola, Habeeb Bello-Salau, Ime Jarlath Umoh, Adeiza James Onumanyi, Mutiu Adesina Adegboye, and Ahmed Tijani Salawudeen. "Simple Deterministic Selection-Based Genetic Algorithm for Hyperparameter Tuning of Machine Learning Models." Applied Sciences 12, no. 3 (2022): 1186. http://dx.doi.org/10.3390/app12031186.

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Hyperparameter tuning is a critical function necessary for the effective deployment of most machine learning (ML) algorithms. It is used to find the optimal hyperparameter settings of an ML algorithm in order to improve its overall output performance. To this effect, several optimization strategies have been studied for fine-tuning the hyperparameters of many ML algorithms, especially in the absence of model-specific information. However, because most ML training procedures need a significant amount of computational time and memory, it is frequently necessary to build an optimization technique

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Muhamad Ridwan and Ema Utami. "An Optimized Hyperparameter Tuning for Improved Hate Speech Detection with Multilayer Perceptron." Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) 8, no. 4 (2024): 525–34. https://doi.org/10.29207/resti.v8i4.5949.

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Hate speech classification is a critical task in the domain of natural language processing, aiming to mitigate the negative impacts of harmful content on digital platforms. This study explores the application of a Multilayer Perceptron (MLP) model for hate speech classification, utilizing Bag of Words (BoW) for feature extraction. The hypothesis posits that hyperparameter tuning through sophisticated optimization techniques will significantly improve model performance. To validate this hypothesis, we employed two distinct hyperparameter tuning approaches: Random Search and Optuna. Random Searc

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Yertayev, Ansar, and Hunaıda Avvad. "Lightweight Hyperparameter Optimization Model for Enhancing Phishing Detection in IoT." Erzincan Üniversitesi Fen Bilimleri Enstitüsü Dergisi 18, no. 1 (2025): 189–203. https://doi.org/10.18185/erzifbed.1574090.

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This study presents an enhanced machine learning approach that emphasizes the optimization of hyperparameters to improve phishing detection, particularly in resource-constrained environments like Internet of Things (IoT) devices. Phishing is considered one of the dangerous forms of cyberattacks where attackers can reveal sensitive information about user's identity, password, privacy and even properties. Machine learning techniques and tools are playing important role in detecting phishing and have shown to be effective and advantageous methods for detection and classification, especially for t

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Aviles, Marcos, Juvenal Rodríguez-Reséndiz, and Danjela Ibrahimi. "Optimizing EMG Classification through Metaheuristic Algorithms." Technologies 11, no. 4 (2023): 87. http://dx.doi.org/10.3390/technologies11040087.

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This work proposes a metaheuristic-based approach to hyperparameter selection in a multilayer perceptron to classify EMG signals. The main goal of the study is to improve the performance of the model by optimizing four important hyperparameters: the number of neurons, the learning rate, the epochs, and the training batches. The approach proposed in this work shows that hyperparameter optimization using particle swarm optimization and the gray wolf optimizer significantly improves the performance of a multilayer perceptron in classifying EMG motion signals. The final model achieves an average c

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Ma, Zhixin, Shengmin Cui, and Inwhee Joe. "An Enhanced Proximal Policy Optimization-Based Reinforcement Learning Method with Random Forest for Hyperparameter Optimization." Applied Sciences 12, no. 14 (2022): 7006. http://dx.doi.org/10.3390/app12147006.

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For most machine learning and deep learning models, the selection of hyperparameters has a significant impact on the performance of the model. Therefore, deep learning and data analysis experts have to spend a lot of time on hyperparameter tuning when building a model for accomplishing a task. Although there are many algorithms used to solve hyperparameter optimization (HPO), these methods require the results of the actual trials at each epoch to help perform the search. To reduce the number of trials, model-based reinforcement learning adopts multilayer perceptron (MLP) to capture the relatio

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Ridho, Akhmad, and Alamsyah Alamsyah. "Chaotic Whale Optimization Algorithm in Hyperparameter Selection in Convolutional Neural Network Algorithm." Journal of Advances in Information Systems and Technology 4, no. 2 (2023): 156–69. http://dx.doi.org/10.15294/jaist.v4i2.60595.

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In several previous studies, metaheuristic methods were used to search for CNN hyperparameters. However, this research only focuses on searching for CNN hyperparameters in the type of network architecture, network structure, and initializing network weights. Therefore, in this article, we only focus on searching for CNN hyperparameters with network architecture type, and network structure with additional regularization. In this article, the CNN hyperparameter search with regularization uses CWOA on the MNIST and FashionMNIST datasets. Each dataset consists of 60,000 training data and 10,000 te

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Jervis, Michael, Mingliang Liu, and Robert Smith. "Deep learning network optimization and hyperparameter tuning for seismic lithofacies classification." Leading Edge 40, no. 7 (2021): 514–23. http://dx.doi.org/10.1190/tle40070514.1.

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Deep learning is increasingly being applied in many aspects of seismic processing and interpretation. Here, we look at a deep convolutional neural network approach to multiclass seismic lithofacies characterization using well logs and seismic data. In particular, we focus on network performance and hyperparameter tuning. Several hyperparameter tuning approaches are compared, including true and directed random search methods such as very fast simulated annealing and Bayesian hyperparameter optimization. The results show that improvements in predictive capability are possible by using automatic

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Bruni, Renato, Gianpiero Bianchi, and Pasquale Papa. "Hyperparameter Black-Box Optimization to Improve the Automatic Classification of Support Tickets." Algorithms 16, no. 1 (2023): 46. http://dx.doi.org/10.3390/a16010046.

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User requests to a customer service, also known as tickets, are essentially short texts in natural language. They should be grouped by topic to be answered efficiently. The effectiveness increases if this semantic categorization becomes automatic. We pursue this goal by using text mining to extract the features from the tickets, and classification to perform the categorization. This is however a difficult multi-class problem, and the classification algorithm needs a suitable hyperparameter configuration to produce a practically useful categorization. As recently highlighted by several research

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Simamora, Fandi Presly, Ronsen Purba, and Muhammad Fermi Pasha. "Optimisasi Hyperparameter BiLSTM Menggunakan Bayesian Optimization untuk Prediksi Harga Saham." Jambura Journal of Mathematics 7, no. 1 (2025): 8–13. https://doi.org/10.37905/jjom.v7i1.27166.

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The accuracy of deep learning models in predicting dynamic and non-linear stock market data highly depends on selecting optimal hyperparameters. However, finding optimal hyperparameters can be costly in terms of the model's objective function, as it requires testing all possible combinations of hyperparameter configurations. This research aims to find the optimal hyperparameter configuration for the BiLSTM model using Bayesian Optimization. The study was conducted using three blue-chip stocks from different sectors, namely BBCA, BYAN, and TLKM, with two scenarios of search iterations. The test

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Johnson, Kara Layne, and Nicole Bohme Carnegie . "Calibration of an Adaptive Genetic Algorithm for Modeling Opinion Diffusion." Algorithms 15, no. 2 (2022): 45. http://dx.doi.org/10.3390/a15020045.

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Genetic algorithms mimic the process of natural selection in order to solve optimization problems with minimal assumptions and perform well when the objective function has local optima on the search space. These algorithms treat potential solutions to the optimization problem as chromosomes, consisting of genes which undergo biologically-inspired operators to identify a better solution. Hyperparameters or control parameters determine the way these operators are implemented. We created a genetic algorithm in order to fit a DeGroot opinion diffusion model using limited data, making use of select

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Abbas, Farkhanda, Feng Zhang, Muhammad Ismail, et al. "Optimizing Machine Learning Algorithms for Landslide Susceptibility Mapping along the Karakoram Highway, Gilgit Baltistan, Pakistan: A Comparative Study of Baseline, Bayesian, and Metaheuristic Hyperparameter Optimization Techniques." Sensors 23, no. 15 (2023): 6843. http://dx.doi.org/10.3390/s23156843.

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Algorithms for machine learning have found extensive use in numerous fields and applications. One important aspect of effectively utilizing these algorithms is tuning the hyperparameters to match the specific task at hand. The selection and configuration of hyperparameters directly impact the performance of machine learning models. Achieving optimal hyperparameter settings often requires a deep understanding of the underlying models and the appropriate optimization techniques. While there are many automatic optimization techniques available, each with its own advantages and disadvantages, this

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Kumar, Suraj, and Kukku Youseff. "Integrated Feature Selection and Hyperparameter Optimization for Multi-Label Classification of Medical Conditions." International Journal of Science and Research (IJSR) 13, no. 3 (2024): 408–13. http://dx.doi.org/10.21275/sr24304214035.

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Asyrofiyyah, Nuril, and Endang Sugiharti. "Hyperparameter Optimization Using Hyperband in Convolutional Neural Network for Image Classification of Indonesian Snacks." Recursive Journal of Informatics 2, no. 1 (2024): 45–53. http://dx.doi.org/10.15294/rji.v2i1.72720.

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Abstract. Indonesia is known for its traditional food both domestically and abroad. Several cakes are included in favorite traditional foods. Of the many types of cakes that exist, it is visually easy to recognize by humans, but computer vision requires special techniques in identifying image objects to types of cakes. Therefore, to recognize objects in the form of images of cakes as one of Indonesian specialties, a deep learning algorithm technique, namely the Convolutional Neural Network (CNN) can be used. Purpose: This study aims to find out how the Convolutional Neural Network (CNN) works

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Lu, Wanjie, Hongpeng Mao, Fanhao Lin, Zilin Chen, Hua Fu, and Yaosong Xu. "Recognition of rolling bearing running state based on genetic algorithm and convolutional neural network." Advances in Mechanical Engineering 14, no. 4 (2022): 168781322210956. http://dx.doi.org/10.1177/16878132221095635.

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In this study, the GA-CNN model is proposed to realize the automatic recognition of rolling bearing running state. Firstly, to avoid the over-fitting and gradient dispersion in the training process of the CNN model, the BN layer and Dropout technology are introduced into the LeNet-5 model. Secondly, to obtain the automatic selection of hyperparameters in CNN model, a method of hyperparameter selection combined with genetic algorithm (GA) is proposed. In the proposed method, each hyperparameter is encoded as a chromosome, and each hyperparameter has a mapping relationship with the corresponding

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Rawat, Waseem, and Zenghui Wang. "Hybrid Stochastic GA-Bayesian Search for Deep Convolutional Neural Network Model Selection." JUCS - Journal of Universal Computer Science 25, no. (6) (2019): 647–66. https://doi.org/10.3217/jucs-025-06-0647.

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In recent years, deep convolutional neural networks (DCNNs) have delivered notable successes in visual tasks, and in particular, image classification related applications. However, they are sensitive to the selection of their architectural and learning hyperparameters, which impose an exponentially large search space on modern DCNN models. Traditional hyperparameter selection methods include manual model tuning, grid, or random search but these require expert domain knowledge or are computationally burdensome. On the other hand, Bayesian optimization and evolutionary inspired techniques have s

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Abu, Masyitah, Nik Adilah Hanin Zahri, Amiza Amir, et al. "A Comprehensive Performance Analysis of Transfer Learning Optimization in Visual Field Defect Classification." Diagnostics 12, no. 5 (2022): 1258. http://dx.doi.org/10.3390/diagnostics12051258.

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Numerous research have demonstrated that Convolutional Neural Network (CNN) models are capable of classifying visual field (VF) defects with great accuracy. In this study, we evaluated the performance of different pre-trained models (VGG-Net, MobileNet, ResNet, and DenseNet) in classifying VF defects and produced a comprehensive comparative analysis to compare the performance of different CNN models before and after hyperparameter tuning and fine-tuning. Using 32 batch sizes, 50 epochs, and ADAM as the optimizer to optimize weight, bias, and learning rate, VGG-16 obtained the highest accuracy

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Hendriks, Jacob, and Patrick Dumond. "Exploring the Relationship between Preprocessing and Hyperparameter Tuning for Vibration-Based Machine Fault Diagnosis Using CNNs." Vibration 4, no. 2 (2021): 284–309. http://dx.doi.org/10.3390/vibration4020019.

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This paper demonstrates the differences between popular transformation-based input representations for vibration-based machine fault diagnosis. This paper highlights the dependency of different input representations on hyperparameter selection with the results of training different configurations of classical convolutional neural networks (CNNs) with three common benchmarking datasets. Raw temporal measurement, Fourier spectrum, envelope spectrum, and spectrogram input types are individually used to train CNNs. Many configurations of CNNs are trained, with variable input sizes, convolutional k

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Kaur, Balraj Preet, Harpreet Singh, Rahul Hans, Sanjeev Kumar Sharma, Chetna Sharma, and Md Mehedi Hassan. "A Genetic algorithm aided hyper parameter optimization based ensemble model for respiratory disease prediction with Explainable AI." PLOS ONE 19, no. 12 (2024): e0308015. https://doi.org/10.1371/journal.pone.0308015.

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In the current era, a lot of research is being done in the domain of disease diagnosis using machine learning. In recent times, one of the deadliest respiratory diseases, COVID-19, which causes serious damage to the lungs has claimed a lot of lives globally. Machine learning-based systems can assist clinicians in the early diagnosis of the disease, which can reduce the deadly effects of the disease. For the successful deployment of these machine learning-based systems, hyperparameter-based optimization and feature selection are important issues. Motivated by the above, in this proposal, we des

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Pardede, Jasman, and Khairul Rijal. "The Effect of Hyperparameters on Faster R-CNN in Face Recognition Systems." Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) 9, no. 3 (2025): 436–48. https://doi.org/10.29207/resti.v9i3.6405.

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Face recognition is one of the main challenges in the development of computer vision technology. This study aims to develop a face recognition system using a Faster R-CNN architecture, optimized through hyperparameter tuning. This research utilizes the "Face Recognition Dataset" from Kaggle, which comprises 2,564 face images across 31 classes. The development process involves creating bounding boxes using the LabelImg application and implementing the Grid Search method. The Grid Search is applied with predefined hyperparameter combinations (3 epochs [10, 25, and 50] × 3 learning rates [0.001,

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Han, Junjie, Cedric Gondro, and Juan Steibel. "98 Using differential evolution to improve predictive accuracy of deep learning models applied to pig production data." Journal of Animal Science 98, Supplement_3 (2020): 27. http://dx.doi.org/10.1093/jas/skaa054.048.

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Abstract Deep learning (DL) is being used for prediction in precision livestock farming and in genomic prediction. However, optimizing hyperparameters in DL models is critical for their predictive performance. Grid search is the traditional approach to select hyperparameters in DL, but it requires exhaustive search over the parameter space. We propose hyperparameter selection using differential evolution (DE), which is a heuristic algorithm that does not require exhaustive search. The goal of this study was to design and apply DE to optimize hyperparameters of DL models for genomic prediction

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Anam, Syaiful, Imam Nurhadi Purwanto, Dwi Mifta Mahanani, Feby Indriana Yusuf, and Hady Rasikhun. "Health Risk Classification Using XGBoost with Bayesian Hyperparameter Optimization." Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) 9, no. 3 (2025): 465–73. https://doi.org/10.29207/resti.v9i3.6307.

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Health risk classification is important. However, health risk classification is challenging to address using conventional analytical techniques. The XGBoost algorithm offers many advantages over the traditional methods for risk classification. Hyperparameter Optimization (HO) of XGBoost is critical for maximizing the performance of the XGBoost algorithm. The manual selection of hyperparameters requires a large amount of time and computational resources. Automatic HO is needed to avoid this problem. Several studies have shown that Bayesian Optimization (BO) works better than Grid Search (GS) or

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Michael, Stefanus, and Amalia Zahra. "Multimodal speech emotion recognition optimization using genetic algorithm." Bulletin of Electrical Engineering and Informatics 13, no. 5 (2024): 3309–16. http://dx.doi.org/10.11591/eei.v13i5.7409.

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Speech emotion recognition (SER) is a technology that can detect emotions in speech. Various methods have been used in developing SER, such as convolutional neural networks (CNNs), long short-term memory (LSTM), and multilayer perceptron. However, sometimes in addition to model selection, other techniques are still needed to improve SER performance, namely optimization methods. This paper compares manual hyperparameter tuning using grid search (GS) and hyperparameter tuning using genetic algorithm (GA) on the LSTM model to prove the performance increase in the multimodal SER model after optimi

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Annaboina, Krishna, Samala Prasoona, Chada Ashritha, and Pesara Chakradhar Reddy. "Fraud Detection in Medical Insurance Claim Systems using Machine Learning." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 01 (2025): 1–9. https://doi.org/10.55041/ijsrem40522.

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Fraud detection in medical insurance claim systems is crucial for preserving healthcare service integrity and minimizing financial losses. This study explores the application of Support Vector Machines (SVM) enhanced by GridSearchCV for hyperparameter optimization, aiming to detect fraudulent claims effectively. The research methodology involves preprocessing a comprehensive medical insurance claims dataset, focusing on extensive feature selection and engineering to improve model performance. GridSearchCV is utilized to conduct an exhaustive search over specified parameter ranges, identifying

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Singh, Sandeep Pratap, and Shamik Tiwari. "Optimizing dual modal biometric authentication: hybrid HPO-ANFIS and HPO-CNN framework." Indonesian Journal of Electrical Engineering and Computer Science 33, no. 3 (2024): 1676. http://dx.doi.org/10.11591/ijeecs.v33.i3.pp1676-1693.

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In the realm of secure data access, biometric authentication frameworks are vital. This work proposes a hybrid model, with a 90% confidence interval, that combines "hyperparameter optimization-adaptive neuro-fuzzy inference system (HPO-ANFIS)" parallel and "hyperparameter optimization-convolutional neural network (HPO-CNN)" sequential techniques. This approach addresses challenges in feature selection, hyperparameter optimization (HPO), and classification in dual multimodal biometric authentication. HPO-ANFIS optimizes feature selection, enhancing discriminative abilities, resulting in improve

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Singh, Sandeep Pratap, and Shamik Tiwari. "Optimizing dual modal biometric authentication: hybrid HPO-ANFIS and HPO-CNN framework." Indonesian Journal of Electrical Engineering and Computer Science 33, no. 3 (2024): 1676–93. https://doi.org/10.11591/ijeecs.v33.i3.pp1676-1693.

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In the realm of secure data access, biometric authentication frameworks are vital. This work proposes a hybrid model, with a 90% confidence interval, that combines "hyperparameter optimization-adaptive neuro-fuzzy inference system (HPO-ANFIS)" parallel and "hyperparameter optimization-convolutional neural network (HPO-CNN)" sequential techniques. This approach addresses challenges in feature selection, hyperparameter optimization (HPO), and classification in dual multimodal biometric authentication. HPO-ANFIS optimizes feature selection, enhancing discriminative abilities, resulting in improve

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Truger, Felix, Martin Beisel, Johanna Barzen, Frank Leymann, and Vladimir Yussupov. "Selection and Optimization of Hyperparameters in Warm-Started Quantum Optimization for the MaxCut Problem." Electronics 11, no. 7 (2022): 1033. http://dx.doi.org/10.3390/electronics11071033.

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Today’s quantum computers are limited in their capabilities, e.g., the size of executable quantum circuits. The Quantum Approximate Optimization Algorithm (QAOA) addresses these limitations and is, therefore, a promising candidate for achieving a near-term quantum advantage. Warm-starting can further improve QAOA by utilizing classically pre-computed approximations to achieve better solutions at a small circuit depth. However, warm-starting requirements often depend on the quantum algorithm and problem at hand. Warm-started QAOA (WS-QAOA) requires developers to understand how to select approac

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Adivarekar1, Pravin P., Amarnath Prabhakaran A, Sukhwinder Sharma, Divya P, Muniyandy Elangovan, and Ravi Rastogi. "Automated machine learning and neural architecture optimization." Scientific Temper 14, no. 04 (2023): 1345–51. http://dx.doi.org/10.58414/scientifictemper.2023.14.4.42.

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Automated machine learning (AutoML) and neural architecture optimization (NAO) represent pivotal components in the landscape of machine learning and artificial intelligence. This paper extensively explores these domains, aiming to delineate their significance, methodologies, cutting-edge techniques, challenges, and emerging trends. AutoML streamlines and democratizes machine learning by automating intricate procedures, such as algorithm selection and hyperparameter tuning. Conversely, NAO automates the design of neural network architectures, a critical aspect for optimizing deep learning model

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Liu, Zifa, Siqi Zheng, and Kunyang Li. "Short-Term Power Load Forecasting Method Based on Feature Selection and Co-Optimization of Hyperparameters." Energies 17, no. 15 (2024): 3712. http://dx.doi.org/10.3390/en17153712.

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The current power load exhibits strong nonlinear and stochastic characteristics, increasing the difficulty of short-term prediction. To more accurately capture data features and enhance prediction accuracy and generalization ability, in this paper, we propose an efficient approach for short-term electric load forecasting that is grounded in a synergistic strategy of feature optimization and hyperparameter tuning. Firstly, a dynamic adjustment strategy based on the rate of the change of historical optimal values is introduced to enhance the PID-based Search Algorithm (PSA), enabling the real-ti

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Sai, Kalyana Pranitha Buddiga. "Optimizing Machine Learning Models: A Comprehensive Overview of Hyperparameter Tuning Techniques." Journal of Scientific and Engineering Research 8, no. 2 (2021): 269–73. https://doi.org/10.5281/zenodo.11216339.

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Hyperparameter tuning is a crucial process for optimizing machine learning models, impacting their performance and generalization ability.  This paper provides a comprehensive overview of various hyperparameter tuning techniques aimed at enhancing the effectiveness and efficiency of machine learning algorithms. In addition to discussing different hyperparameter tuning techniques, the paper also explores the challenges associated with hyperparameter optimization and applicability in different scenarios. By delving into best practices, this paper equips researchers and practitioners with th

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Zhang, Shuangbo. "Automatic Selection and Parameter Optimization of Mathematical Models Based on Machine Learning." Transactions on Computer Science and Intelligent Systems Research 3 (April 10, 2024): 34–39. http://dx.doi.org/10.62051/nx5n1v79.

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With the rapid progress of machine learning (ML) technology, more and more ML algorithms have emerged, and the complexity of models is also constantly increasing. This development trend brings two significant challenges in practice: how to choose appropriate algorithm models and how to optimize hyperparameters for these models. In this context, the concept of Automatic Machine Learning (AutoML) has emerged. Due to the applicability of different algorithm models to different data types and problem scenarios, it is crucial to automatically select the most suitable model based on the characterist

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Badriyah, Tessy, Dimas Bagus Santoso, Iwan Syarif, and Daisy Rahmania Syarif. "Improving stroke diagnosis accuracy using hyperparameter optimized deep learning." International Journal of Advances in Intelligent Informatics 5, no. 3 (2019): 256. http://dx.doi.org/10.26555/ijain.v5i3.427.

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Stroke may cause death for anyone, including youngsters. One of the early stroke detection techniques is a Computerized Tomography (CT) scan. This research aimed to optimize hyperparameter in Deep Learning, Random Search and Bayesian Optimization for determining the right hyperparameter. The CT scan images were processed by scaling, grayscale, smoothing, thresholding, and morphological operation. Then, the images feature was extracted by the Gray Level Co-occurrence Matrix (GLCM). This research was performed a feature selection to select relevant features for reducing computing expenses, while

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T., Aditya Sai Srinivas, and Bharathi M. "FLAML: The Python Wizardry for Automated Machine Learning." Recent Trends in Cloud Computing and Web Engineering 6, no. 1 (2023): 24–27. https://doi.org/10.5281/zenodo.10431828.

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<em>FLAML (Fast and Lightweight AutoML) is a powerful Python library designed to automate the process of hyperparameter tuning and model selection in machine learning tasks. This tutorial provides a comprehensive guide to using FLAML effectively. It covers the installation and setup process, loading datasets, and configuring optimization options. Participants will learn how FLAML employs advanced search strategies, such as Bayesian optimization, to efficiently explore hyperparameter spaces. Additionally, the tutorial demonstrates how FLAML automatically selects the most suitable machine learni

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Pratomo, Awang Hendrianto, Nur Heri Cahyana, and Septi Nur Indrawati. "Optimizing CNN hyperparameters with genetic algorithms for face mask usage classification." Science in Information Technology Letters 4, no. 1 (2023): 54–64. http://dx.doi.org/10.31763/sitech.v4i1.1182.

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Convolutional Neural Networks (CNNs) have gained significant traction in the field of image categorization, particularly in the domains of health and safety. This study aims to categorize the utilization of face masks, which is a vital determinant of respiratory health. Convolutional neural networks (CNNs) possess a high level of complexity, making it crucial to execute hyperparameter adjustment in order to optimize the performance of the model. The conventional approach of trial-and-error hyperparameter configuration often yields suboptimal outcomes and is time-consuming. Genetic Algorithms (

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Bergstra, James, Brent Komer, Chris Eliasmith, Dan Yamins, and David D. Cox. "Hyperopt: a Python library for model selection and hyperparameter optimization." Computational Science & Discovery 8, no. 1 (2015): 014008. http://dx.doi.org/10.1088/1749-4699/8/1/014008.

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Loukili, Manal. "Supervised Learning Algorithms for Predicting Customer Churn with Hyperparameter Optimization." International Journal of Advances in Soft Computing and its Applications 14, no. 3 (2022): 50–63. http://dx.doi.org/10.15849/ijasca.221128.04.

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Abstract Churn risk is one of the most worrying issues in the telecommunications industry. The methods for predicting churn have been improved to a great extent by the remarkable developments in the word of artificial intelligence and machine learning. In this context, a comparative study of four machine learning models was conducted. The first phase consists of data preprocessing, followed by feature analysis. In the third phase, feature selection. Then, the data is split into the training set and the test set. During the prediction phase, some of the commonly used predictive models were adop

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Zhang, Xuan, and Kevin Duh. "Reproducible and Efficient Benchmarks for Hyperparameter Optimization of Neural Machine Translation Systems." Transactions of the Association for Computational Linguistics 8 (July 2020): 393–408. http://dx.doi.org/10.1162/tacl_a_00322.

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Hyperparameter selection is a crucial part of building neural machine translation (NMT) systems across both academia and industry. Fine-grained adjustments to a model’s architecture or training recipe can mean the difference between a positive and negative research result or between a state-of-the-art and underperforming system. While recent literature has proposed methods for automatic hyperparameter optimization (HPO), there has been limited work on applying these methods to neural machine translation (NMT), due in part to the high costs associated with experiments that train large numbers o

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