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Cristina Cañero, Nikolaos Thomos, George A. Triantafyllid, George C. Litos, & Michael G. Strintzis. (2005). Mobile Tele-echography: User Interface Design. IEEE Transactions on Information Technology in Biomedicine, 9(1):44–49 (IF: 1.376).
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David Masip, & Jordi Vitria. (2008). Shared Feature Extraction for Nearest Neighbor Face Recognition. IEEE Transactions on Neural Networks, 586–595.
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David Masip, & Jordi Vitria. (2006). Boosted discriminant projections for nearest neighbor classification. Pattern Recognition, 39(2): 164–170.
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David Masip, & Jordi Vitria. (2005). Feature Extraction for Nearest Neighbor Classification. Application to Gender Recognition. International Journal of Intelligent Systems, 20(5): 561–576 (IF: 0.657).
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David Masip, Ludmila I. Kuncheva, & Jordi Vitria. (2005). An ensemble-based method for linear feature extraction for two-class problems. Pattern Analysis and Applications, 8(3): 227–237 (IF: 0.782).
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Diego Velazquez, Pau Rodriguez, Alexandre Lacoste, Issam H. Laradji, Xavier Roca, & Jordi Gonzalez. (2023). Evaluating Counterfactual Explainers. TMLR - Transactions on Machine Learning Research.
Abstract: Explainability methods have been widely used to provide insight into the decisions made by statistical models, thus facilitating their adoption in various domains within the industry. Counterfactual explanation methods aim to improve our understanding of a model by perturbing samples in a way that would alter its response in an unexpected manner. This information is helpful for users and for machine learning practitioners to understand and improve their models. Given the value provided by counterfactual explanations, there is a growing interest in the research community to investigate and propose new methods. However, we identify two issues that could hinder the progress in this field. (1) Existing metrics do not accurately reflect the value of an explainability method for the users. (2) Comparisons between methods are usually performed with datasets like CelebA, where images are annotated with attributes that do not fully describe them and with subjective attributes such as ``Attractive''. In this work, we address these problems by proposing an evaluation method with a principled metric to evaluate and compare different counterfactual explanation methods. The evaluation method is based on a synthetic dataset where images are fully described by their annotated attributes. As a result, we are able to perform a fair comparison of multiple explainability methods in the recent literature, obtaining insights about their performance. We make the code public for the benefit of the research community.
Keywords: Explainability; Counterfactuals; XAI
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