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Bibliografias temáticas / Explainability of machine learning models

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Literatura científica selecionada sobre o tema "Explainability of machine learning models"

Autor: Grafiati

Publicado: 11 de janeiro de 2025

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Artigos de revistas sobre o assunto "Explainability of machine learning models"

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S, Akshay, and Manu Madhavan. "COMPARISON OF EXPLAINABILITY OF MACHINE LEARNING BASED MALAYALAM TEXT CLASSIFICATION." ICTACT Journal on Soft Computing 15, no. 1 (2024): 3386–91. http://dx.doi.org/10.21917/ijsc.2024.0476.

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Text classification is one of the primary NLP tasks where machine learning (ML) is widely used. Even though the applied machine learning models are similar, the classification task may address specific challenges from language to language. The concept of model explainability can provide an idea of how the models make decisions in these situations. In this paper, The explainability of different text classification models for Malayalam language, a morphologically rich Dravidian language predominantly spoken in Kerala, was compared. The experiments considered classification models from both tradi

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Park, Min Sue, Hwijae Son, Chongseok Hyun, and Hyung Ju Hwang. "Explainability of Machine Learning Models for Bankruptcy Prediction." IEEE Access 9 (2021): 124887–99. http://dx.doi.org/10.1109/access.2021.3110270.

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Cheng, Xueyi, and Chang Che. "Interpretable Machine Learning: Explainability in Algorithm Design." Journal of Industrial Engineering and Applied Science 2, no. 6 (2024): 65–70. https://doi.org/10.70393/6a69656173.323337.

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In recent years, there is a high demand for transparency and accountability in machine learning models, especially in domains such as healthcare, finance and etc. In this paper, we delve into deep how to make machine learning models more interpretable, with focus on the importance of the explainability of the algorithm design. The main objective of this paper is to fill this gap and provide a comprehensive survey and analytical study towards AutoML. To that end, we first introduce the AutoML technology and review its various tools and techniques.

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Bozorgpanah, Aso, Vicenç Torra, and Laya Aliahmadipour. "Privacy and Explainability: The Effects of Data Protection on Shapley Values." Technologies 10, no. 6 (2022): 125. http://dx.doi.org/10.3390/technologies10060125.

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There is an increasing need to provide explainability for machine learning models. There are different alternatives to provide explainability, for example, local and global methods. One of the approaches is based on Shapley values. Privacy is another critical requirement when dealing with sensitive data. Data-driven machine learning models may lead to disclosure. Data privacy provides several methods for ensuring privacy. In this paper, we study how methods for explainability based on Shapley values are affected by privacy methods. We show that some degree of protection still permits to mainta

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Zhang, Xueting. "Traffic Flow Prediction Based on Explainable Machine Learning." Highlights in Science, Engineering and Technology 56 (July 14, 2023): 56–64. http://dx.doi.org/10.54097/hset.v56i.9816.

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Traffic flow prediction is one of the important links to realize an urban intelligent transportation system. Thanks to the in-depth research of artificial intelligence theories, the machine learning method has been widely used in intelligent transportation engineering. However, due to the “black box” as its characteristics, its application and further development are limited. Exploring the explainability of machine learning models in traffic flow prediction is an important issue to make it more reliable in traffic engineering and other practical applications. Apart from selecting the RandomFor

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Pendyala, Vishnu, and Hyungkyun Kim. "Assessing the Reliability of Machine Learning Models Applied to the Mental Health Domain Using Explainable AI." Electronics 13, no. 6 (2024): 1025. http://dx.doi.org/10.3390/electronics13061025.

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Machine learning is increasingly and ubiquitously being used in the medical domain. Evaluation metrics like accuracy, precision, and recall may indicate the performance of the models but not necessarily the reliability of their outcomes. This paper assesses the effectiveness of a number of machine learning algorithms applied to an important dataset in the medical domain, specifically, mental health, by employing explainability methodologies. Using multiple machine learning algorithms and model explainability techniques, this work provides insights into the models’ workings to help determine th

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Kim, Dong-sup, and Seungwoo Shin. "THE ECONOMIC EXPLAINABILITY OF MACHINE LEARNING AND STANDARD ECONOMETRIC MODELS-AN APPLICATION TO THE U.S. MORTGAGE DEFAULT RISK." International Journal of Strategic Property Management 25, no. 5 (2021): 396–412. http://dx.doi.org/10.3846/ijspm.2021.15129.

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This study aims to bridge the gap between two perspectives of explainability−machine learning and engineering, and economics and standard econometrics−by applying three marginal measurements. The existing real estate literature has primarily used econometric models to analyze the factors that affect the default risk of mortgage loans. However, in this study, we estimate a default risk model using a machine learning-based approach with the help of a U.S. securitized mortgage loan database. Moreover, we compare the economic explainability of the models by calculating the marginal effect and marg

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TOPCU, Deniz. "How to explain a machine learning model: HbA1c classification example." Journal of Medicine and Palliative Care 4, no. 2 (2023): 117–25. http://dx.doi.org/10.47582/jompac.1259507.

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Aim: Machine learning tools have various applications in healthcare. However, the implementation of developed models is still limited because of various challenges. One of the most important problems is the lack of explainability of machine learning models. Explainability refers to the capacity to reveal the reasoning and logic behind the decisions made by AI systems, making it straightforward for human users to understand the process and how the system arrived at a specific outcome. The study aimed to compare the performance of different model-agnostic explanation methods using two different

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Rodríguez Mallma, Mirko Jerber, Luis Zuloaga-Rotta, Rubén Borja-Rosales, et al. "Explainable Machine Learning Models for Brain Diseases: Insights from a Systematic Review." Neurology International 16, no. 6 (2024): 1285–307. http://dx.doi.org/10.3390/neurolint16060098.

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In recent years, Artificial Intelligence (AI) methods, specifically Machine Learning (ML) models, have been providing outstanding results in different areas of knowledge, with the health area being one of its most impactful fields of application. However, to be applied reliably, these models must provide users with clear, simple, and transparent explanations about the medical decision-making process. This systematic review aims to investigate the use and application of explainability in ML models used in brain disease studies. A systematic search was conducted in three major bibliographic data

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Bhagyashree D Shendkar. "Explainable Machine Learning Models for Real-Time Threat Detection in Cybersecurity." Panamerican Mathematical Journal 35, no. 1s (2024): 264–75. http://dx.doi.org/10.52783/pmj.v35.i1s.2313.

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In the rapidly evolving landscape of cybersecurity, traditional machine learning models often operate as "black boxes," providing high accuracy but lacking transparency in decision-making. This lack of explainability poses challenges for trust and accountability, especially in critical areas like threat detection and incident response. Explainable machine learning models aim to address this by making the model's predictions more understandable and interpretable to users. This research integrates explainable machine learning models for real-time threat detection in cybersecurity. Data from mult

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Teses / dissertações sobre o assunto "Explainability of machine learning models"

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Delaunay, Julien. "Explainability for machine learning models : from data adaptability to user perception." Electronic Thesis or Diss., Université de Rennes (2023-....), 2023. http://www.theses.fr/2023URENS076.

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Cette thèse se concentre sur la génération d'explications locales pour les modèles de machine learning déjà déployés, en recherchant les conditions optimales pour des explications pertinentes, prenant en compte à la fois les données et les besoins de l'utilisateur. L'objectif principal est de développer des méthodes produisant des explications pour n'importe quel modèle de prédiction, tout en veillant à ce que ces explications demeurent à la fois fidèles au modèle sous-jacent et compréhensibles par les utilisateurs qui les reçoivent. La thèse est divisée en deux parties. Dans la première, on a

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Stanzione, Vincenzo Maria. "Developing a new approach for machine learning explainability combining local and global model-agnostic approaches." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2022. http://amslaurea.unibo.it/25480/.

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The last couple of past decades have seen a new flourishing season for the Artificial Intelligence, in particular for Machine Learning (ML). This is reflected in the great number of fields that are employing ML solutions to overcome a broad spectrum of problems. However, most of the last employed ML models have a black-box behavior. This means that given a certain input, we are not able to understand why one of these models produced a certain output or made a certain decision. Most of the time, we are not interested in knowing what and how the model is thinking, but if we think of a mo

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Ayad, Célia. "Towards Reliable Post Hoc Explanations for Machine Learning on Tabular Data and their Applications." Electronic Thesis or Diss., Institut polytechnique de Paris, 2024. http://www.theses.fr/2024IPPAX082.

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Alors que l’apprentissage automatique continue de démontrer de solides capacités prédictives, il est devenu un outil très précieux dans plusieurs domaines scientifiques et industriels. Cependant, à mesure que les modèles ML évoluent pour atteindre une plus grande précision, ils deviennent également de plus en plus complexes et nécessitent davantage de paramètres.Être capable de comprendre les complexités internes et d’établir une confiance dans les prédictions de ces modèles d’apprentissage automatique est donc devenu essentiel dans divers domaines critiques, notamment la santé et la finance.L

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Radulovic, Nedeljko. "Post-hoc Explainable AI for Black Box Models on Tabular Data." Electronic Thesis or Diss., Institut polytechnique de Paris, 2023. http://www.theses.fr/2023IPPAT028.

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Les modèles d'intelligence artificielle (IA) actuels ont fait leurs preuves dans la résolution de diverses tâches, telles que la classification, la régression, le traitement du langage naturel (NLP) et le traitement d'images. Les ressources dont nous disposons aujourd'hui nous permettent d'entraîner des modèles d'IA très complexes pour résoudre différents problèmes dans presque tous les domaines : médecine, finance, justice, transport, prévisions, etc. Avec la popularité et l'utilisation généralisée des modèles d'IA, la nécessite d'assurer la confiance dans ces modèles s'est également accrue.

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Willot, Hénoïk. "Certified explanations of robust models." Electronic Thesis or Diss., Compiègne, 2024. http://www.theses.fr/2024COMP2812.

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Avec l'utilisation croissante des systèmes d'aide à la décision, automatisés ou semi-automatisés, en intelligence artificielle se crée le besoin de les rendre fiables et transparents pour un utilisateur final. Tandis que le rôle des méthodes d'explicabilité est généralement d'augmenter la transparence, la fiabilité peut être obtenue en fournissant des explications certifiées, dans le sens qu'elles sont garanties d'être vraies, et en considérant des modèles robustes qui peuvent s'abstenir quand l'information disponible est trop insuffisante, plutôt que de forcer une décision dans l'unique but d

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Kurasinski, Lukas. "Machine Learning explainability in text classification for Fake News detection." Thesis, Malmö universitet, Fakulteten för teknik och samhälle (TS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:mau:diva-20058.

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Fake news detection gained an interest in recent years. This made researchers try to findmodels that can classify text in the direction of fake news detection. While new modelsare developed, researchers mostly focus on the accuracy of a model. There is little researchdone in the subject of explainability of Neural Network (NN) models constructed for textclassification and fake news detection. When trying to add a level of explainability to aNeural Network model, allot of different aspects have to be taken under consideration.Text length, pre-processing, and complexity play an important role in

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Lounici, Sofiane. "Watermarking machine learning models." Electronic Thesis or Diss., Sorbonne université, 2022. https://accesdistant.sorbonne-universite.fr/login?url=https://theses-intra.sorbonne-universite.fr/2022SORUS282.pdf.

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La protection de la propriété intellectuelle des modèles d’apprentissage automatique apparaît de plus en plus nécessaire, au vu des investissements et de leur impact sur la société. Dans cette thèse, nous proposons d’étudier le tatouage de modèles d’apprentissage automatique. Nous fournissons un état de l’art sur les techniques de tatouage actuelles, puis nous le complétons en considérant le tatouage de modèles au-delà des tâches de classification d’images. Nous définissons ensuite les attaques de contrefaçon contre le tatouage pour les plateformes d’hébergement de modèles, et nous présentons

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Maltbie, Nicholas. "Integrating Explainability in Deep Learning Application Development: A Categorization and Case Study." University of Cincinnati / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1623169431719474.

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Hardoon, David Roi. "Semantic models for machine learning." Thesis, University of Southampton, 2006. https://eprints.soton.ac.uk/262019/.

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In this thesis we present approaches to the creation and usage of semantic models by the analysis of the data spread in the feature space. We aim to introduce the general notion of using feature selection techniques in machine learning applications. The applied approaches obtain new feature directions on data, such that machine learning applications would show an increase in performance. We review three principle methods that are used throughout the thesis. Firstly Canonical Correlation Analysis (CCA), which is a method of correlating linear relationships between two multidimensional variables

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BODINI, MATTEO. "DESIGN AND EXPLAINABILITY OF MACHINE LEARNING ALGORITHMS FOR THE CLASSIFICATION OF CARDIAC ABNORMALITIES FROM ELECTROCARDIOGRAM SIGNALS." Doctoral thesis, Università degli Studi di Milano, 2022. http://hdl.handle.net/2434/888002.

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The research activity contained in the present thesis work is devoted to the development of novel Machine Learning (ML) and Deep Learning (DL) algorithms for the classification of Cardiac Abnormalities (CA) from Electrocardiogram (ECG) signals, along with the explanation of classification outputs with explainable approaches. Automated computer programs for ECG classification have been developed since 1950s to improve the correct interpretation of the ECG, nowadays facilitating health care decision-making by reducing costs and human errors. The first ECG interpretation computer programs were es

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Livros sobre o assunto "Explainability of machine learning models"

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Nandi, Anirban, and Aditya Kumar Pal. Interpreting Machine Learning Models. Apress, 2022. http://dx.doi.org/10.1007/978-1-4842-7802-4.

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Bolc, Leonard. Computational Models of Learning. Springer Berlin Heidelberg, 1987.

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Galindez Olascoaga, Laura Isabel, Wannes Meert, and Marian Verhelst. Hardware-Aware Probabilistic Machine Learning Models. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-74042-9.

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Singh, Pramod. Deploy Machine Learning Models to Production. Apress, 2021. http://dx.doi.org/10.1007/978-1-4842-6546-8.

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Zhang, Zhihua. Statistical Machine Learning: Foundations, Methodologies and Models. John Wiley & Sons, Limited, 2017.

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Rendell, Larry. Representations and models for concept learning. Dept. of Computer Science, University of Illinois at Urbana-Champaign, 1987.

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Ehteram, Mohammad, Zohreh Sheikh Khozani, Saeed Soltani-Mohammadi, and Maliheh Abbaszadeh. Estimating Ore Grade Using Evolutionary Machine Learning Models. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-8106-7.

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Bisong, Ekaba. Building Machine Learning and Deep Learning Models on Google Cloud Platform. Apress, 2019. http://dx.doi.org/10.1007/978-1-4842-4470-8.

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Gupta, Punit, Mayank Kumar Goyal, Sudeshna Chakraborty, and Ahmed A. Elngar. Machine Learning and Optimization Models for Optimization in Cloud. Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003185376.

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Suthaharan, Shan. Machine Learning Models and Algorithms for Big Data Classification. Springer US, 2016. http://dx.doi.org/10.1007/978-1-4899-7641-3.

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Capítulos de livros sobre o assunto "Explainability of machine learning models"

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Nandi, Anirban, and Aditya Kumar Pal. "The Eight Pitfalls of Explainability Methods." In Interpreting Machine Learning Models. Apress, 2022. http://dx.doi.org/10.1007/978-1-4842-7802-4_15.

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Nandi, Anirban, and Aditya Kumar Pal. "Explainability Facts: A Framework for Systematic Assessment of Explainable Approaches." In Interpreting Machine Learning Models. Apress, 2022. http://dx.doi.org/10.1007/978-1-4842-7802-4_6.

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Kamath, Uday, and John Liu. "Pre-model Interpretability and Explainability." In Explainable Artificial Intelligence: An Introduction to Interpretable Machine Learning. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-83356-5_2.

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Dessain, Jean, Nora Bentaleb, and Fabien Vinas. "Cost of Explainability in AI: An Example with Credit Scoring Models." In Communications in Computer and Information Science. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-44064-9_26.

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AbstractThis paper examines the cost of explainability in machine learning models for credit scoring. The analysis is conducted under the constraint of meeting the regulatory requirements of the European Central Bank (ECB), using a real-life dataset of over 50,000 credit exposures. We compare the statistical and financial performances of black-box models, such as XGBoost and neural networks, with inherently explainable models like logistic regression and GAMs. Notably, statistical performance does not necessarily correlate with financial performance. Our results reveal a difference of 15 to 20 basis points in annual return on investment between the best performing black-box model and the best performing inherently explainable model, as cost of explainability. We also find that the cost of explainability increases together with the risk appetite.To enhance the interpretability of explainable models, we apply isotonic smoothing of features’ shape functions based on expert judgment. Our findings suggest that incorporating expert judgment in the form of isotonic smoothing improves the explainability without compromising the performance. These results have significant implications for the use of explainable models in credit risk assessment and for regulatory compliance.

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Henriques, J., T. Rocha, P. de Carvalho, C. Silva, and S. Paredes. "Interpretability and Explainability of Machine Learning Models: Achievements and Challenges." In IFMBE Proceedings. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-59216-4_9.

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Bargal, Sarah Adel, Andrea Zunino, Vitali Petsiuk, et al. "Beyond the Visual Analysis of Deep Model Saliency." In xxAI - Beyond Explainable AI. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-04083-2_13.

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AbstractIncreased explainability in machine learning is traditionally associated with lower performance, e.g. a decision tree is more explainable, but less accurate than a deep neural network. We argue that, in fact, increasing the explainability of a deep classifier can improve its generalization. In this chapter, we survey a line of our published work that demonstrates how spatial and spatiotemporal visual explainability can be obtained, and how such explainability can be used to train models that generalize better on unseen in-domain and out-of-domain samples, refine fine-grained classification predictions, better utilize network capacity, and are more robust to network compression.

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Stevens, Alexander, Johannes De Smedt, and Jari Peeperkorn. "Quantifying Explainability in Outcome-Oriented Predictive Process Monitoring." In Lecture Notes in Business Information Processing. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-98581-3_15.

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AbstractThe growing interest in applying machine and deep learning algorithms in an Outcome-Oriented Predictive Process Monitoring (OOPPM) context has recently fuelled a shift to use models from the explainable artificial intelligence (XAI) paradigm, a field of study focused on creating explainability techniques on top of AI models in order to legitimize the predictions made. Nonetheless, most classification models are evaluated primarily on a performance level, where XAI requires striking a balance between either simple models (e.g. linear regression) or models using complex inference structures (e.g. neural networks) with post-processing to calculate feature importance. In this paper, a comprehensive overview of predictive models with varying intrinsic complexity are measured based on explainability with model-agnostic quantitative evaluation metrics. To this end, explainability is designed as a symbiosis between interpretability and faithfulness and thereby allowing to compare inherently created explanations (e.g. decision tree rules) with post-hoc explainability techniques (e.g. Shapley values) on top of AI models. Moreover, two improved versions of the logistic regression model capable of capturing non-linear interactions and both inherently generating their own explanations are proposed in the OOPPM context. These models are benchmarked with two common state-of-the-art models with post-hoc explanation techniques in the explainability-performance space.

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Baniecki, Hubert, Wojciech Kretowicz, and Przemyslaw Biecek. "Fooling Partial Dependence via Data Poisoning." In Machine Learning and Knowledge Discovery in Databases. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-26409-2_8.

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AbstractMany methods have been developed to understand complex predictive models and high expectations are placed on post-hoc model explainability. It turns out that such explanations are not robust nor trustworthy, and they can be fooled. This paper presents techniques for attacking Partial Dependence (plots, profiles, PDP), which are among the most popular methods of explaining any predictive model trained on tabular data. We showcase that PD can be manipulated in an adversarial manner, which is alarming, especially in financial or medical applications where auditability became a must-have trait supporting black-box machine learning. The fooling is performed via poisoning the data to bend and shift explanations in the desired direction using genetic and gradient algorithms. We believe this to be the first work using a genetic algorithm for manipulating explanations, which is transferable as it generalizes both ways: in a model-agnostic and an explanation-agnostic manner.

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Colosimo, Bianca Maria, and Fabio Centofanti. "Model Interpretability, Explainability and Trust for Manufacturing 4.0." In Interpretability for Industry 4.0 : Statistical and Machine Learning Approaches. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-12402-0_2.

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Santos, Geanderson, Amanda Santana, Gustavo Vale, and Eduardo Figueiredo. "Yet Another Model! A Study on Model’s Similarities for Defect and Code Smells." In Fundamental Approaches to Software Engineering. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-30826-0_16.

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AbstractSoftware defect and code smell prediction help developers identify problems in the code and fix them before they degrade the quality or the user experience. The prediction of software defects and code smells is challenging, since it involves many factors inherent to the development process. Many studies propose machine learning models for defects and code smells. However, we have not found studies that explore and compare these machine learning models, nor that focus on the explainability of the models. This analysis allows us to verify which features and quality attributes influence software defects and code smells. Hence, developers can use this information to predict if a class may be faulty or smelly through the evaluation of a few features and quality attributes. In this study, we fill this gap by comparing machine learning models for predicting defects and seven code smells. We trained in a dataset composed of 19,024 classes and 70 software features that range from different quality attributes extracted from 14 Java open-source projects. We then ensemble five machine learning models and employed explainability concepts to explore the redundancies in the models using the top-10 software features and quality attributes that are known to contribute to the defects and code smell predictions. Furthermore, we conclude that although the quality attributes vary among the models, the complexity, documentation, and size are the most relevant. More specifically, Nesting Level Else-If is the only software feature relevant to all models.

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Trabalhos de conferências sobre o assunto "Explainability of machine learning models"

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Bouzid, Mohamed, and Manar Amayri. "Addressing Explainability in Load Forecasting Using Time Series Machine Learning Models." In 2024 IEEE 12th International Conference on Smart Energy Grid Engineering (SEGE). IEEE, 2024. http://dx.doi.org/10.1109/sege62220.2024.10739606.

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Burgos, David, Ahsan Morshed, MD Mamunur Rashid, and Satria Mandala. "A Comparison of Machine Learning Models to Deep Learning Models for Cancer Image Classification and Explainability of Classification." In 2024 International Conference on Data Science and Its Applications (ICoDSA). IEEE, 2024. http://dx.doi.org/10.1109/icodsa62899.2024.10651790.

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Sheikhani, Arman, Ervin Agic, Mahshid Helali Moghadam, Juan Carlos Andresen, and Anders Vesterberg. "Lithium-Ion Battery SOH Forecasting: From Deep Learning Augmented by Explainability to Lightweight Machine Learning Models." In 2024 IEEE 29th International Conference on Emerging Technologies and Factory Automation (ETFA). IEEE, 2024. http://dx.doi.org/10.1109/etfa61755.2024.10710794.

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Mechouche, Ammar, Valerio Camerini, Caroline Del, Elsa Cansell, and Konstanca Nikolajevic. "From Dampers Estimated Loads to In-Service Degradation Correlations." In Vertical Flight Society 80th Annual Forum & Technology Display. The Vertical Flight Society, 2024. http://dx.doi.org/10.4050/f-0080-2024-1108.

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This paper presents an original method that takes advantage of existing large in-service flight data, damper load Machine Learning models as well as the inventory of degraded dampers (elastomeric part), to link the estimated loads and operational conditions to damper degradation cases. The Machine Learning models are trained on flight test campaigns data, and then applied on in-service helicopter data to estimate damper loads as a function of flight parameters. The estimated load history is then used as an input to generate engineering load indicators. These latter, jointly with operational an

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Izza, Yacine, Xuanxiang Huang, Antonio Morgado, Jordi Planes, Alexey Ignatiev, and Joao Marques-Silva. "Distance-Restricted Explanations: Theoretical Underpinnings & Efficient Implementation." In 21st International Conference on Principles of Knowledge Representation and Reasoning {KR-2023}. International Joint Conferences on Artificial Intelligence Organization, 2024. http://dx.doi.org/10.24963/kr.2024/45.

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The uses of machine learning (ML) have snowballed in recent years. In many cases, ML models are highly complex, and their operation is beyond the understanding of human decision-makers. Nevertheless, some uses of ML models involve high-stakes and safety-critical applications. Explainable artificial intelligence (XAI) aims to help human decision-makers in understanding the operation of such complex ML models, thus eliciting trust in their operation. Unfortunately, the majority of past XAI work is based on informal approaches, that offer no guarantees of rigor. Unsurprisingly, there exists compr

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Alami, Amine, Jaouad Boumhidi, and Loqman Chakir. "Explainability in CNN based Deep Learning models for medical image classification." In 2024 International Conference on Intelligent Systems and Computer Vision (ISCV). IEEE, 2024. http://dx.doi.org/10.1109/iscv60512.2024.10620149.

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Rodríguez-Barroso, Nuria, Javier Del Ser, M. Victoria Luzón, and Francisco Herrera. "Defense Strategy against Byzantine Attacks in Federated Machine Learning: Developments towards Explainability." In 2024 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2024. http://dx.doi.org/10.1109/fuzz-ieee60900.2024.10611769.

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Perikos, Isidoros. "Sensitive Content Detection in Social Networks Using Deep Learning Models and Explainability Techniques." In 2024 IEEE/ACIS 9th International Conference on Big Data, Cloud Computing, and Data Science (BCD). IEEE, 2024. http://dx.doi.org/10.1109/bcd61269.2024.10743081.

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Gafur, Jamil, Steve Goddard, and William Lai. "Adversarial Robustness and Explainability of Machine Learning Models." In PEARC '24: Practice and Experience in Advanced Research Computing. ACM, 2024. http://dx.doi.org/10.1145/3626203.3670522.

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Islam, Md Ariful, Kowshik Nittala, and Garima Bajwa. "Adding Explainability to Machine Learning Models to Detect Chronic Kidney Disease." In 2022 IEEE 23rd International Conference on Information Reuse and Integration for Data Science (IRI). IEEE, 2022. http://dx.doi.org/10.1109/iri54793.2022.00069.

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Relatórios de organizações sobre o assunto "Explainability of machine learning models"

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Smith, Michael, Erin Acquesta, Arlo Ames, et al. SAGE Intrusion Detection System: Sensitivity Analysis Guided Explainability for Machine Learning. Office of Scientific and Technical Information (OSTI), 2021. http://dx.doi.org/10.2172/1820253.

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Skryzalin, Jacek, Kenneth Goss, and Benjamin Jackson. Securing machine learning models. Office of Scientific and Technical Information (OSTI), 2020. http://dx.doi.org/10.2172/1661020.

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Martinez, Carianne, Jessica Jones, Drew Levin, Nathaniel Trask, and Patrick Finley. Physics-Informed Machine Learning for Epidemiological Models. Office of Scientific and Technical Information (OSTI), 2020. http://dx.doi.org/10.2172/1706217.

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Lavender, Samantha, and Trent Tinker, eds. Testbed-19: Machine Learning Models Engineering Report. Open Geospatial Consortium, Inc., 2024. http://dx.doi.org/10.62973/23-033.

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Saenz, Juan Antonio, Ismael Djibrilla Boureima, Vitaliy Gyrya, and Susan Kurien. Machine-Learning for Rapid Optimization of Turbulence Models. Office of Scientific and Technical Information (OSTI), 2020. http://dx.doi.org/10.2172/1638623.

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Kulkarni, Sanjeev R. Extending and Unifying Formal Models for Machine Learning. Defense Technical Information Center, 1997. http://dx.doi.org/10.21236/ada328730.

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Banerjee, Boudhayan. Machine Learning Models for Political Video Advertisement Classification. Iowa State University, 2017. http://dx.doi.org/10.31274/cc-20240624-976.

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Valaitis, Vytautas, and Alessandro T. Villa. A Machine Learning Projection Method for Macro-Finance Models. Federal Reserve Bank of Chicago, 2022. http://dx.doi.org/10.21033/wp-2022-19.

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Fessel, Kimberly. Machine Learning in Python. Instats Inc., 2024. http://dx.doi.org/10.61700/s74zy0ivgwioe1764.

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This intensive, hands-on workshop offers a deep dive into machine learning with Python, designed for PhD students, professors, and researchers across various fields. Participants will master practical skills in data cleaning, exploratory data analysis, and building powerful machine learning models, including neural networks, to elevate their research. With real-world coding exercises and expert guidance, this workshop will equip you with the tools to turn data into actionable insights.

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Ogunbire, Abimbola, Panick Kalambay, Hardik Gajera, and Srinivas Pulugurtha. Deep Learning, Machine Learning, or Statistical Models for Weather-related Crash Severity Prediction. Mineta Transportation Institute, 2023. http://dx.doi.org/10.31979/mti.2023.2320.

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Nearly 5,000 people are killed and more than 418,000 are injured in weather-related traffic incidents each year. Assessments of the effectiveness of statistical models applied to crash severity prediction compared to machine learning (ML) and deep learning techniques (DL) help researchers and practitioners know what models are most effective under specific conditions. Given the class imbalance in crash data, the synthetic minority over-sampling technique for nominal (SMOTE-N) data was employed to generate synthetic samples for the minority class. The ordered logit model (OLM) and the ordered p

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