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Journal Articles IEEE Journal of Biomedical and Health Informatics Year : 2023

Explanations as a New Metric for Feature Selection: A Systematic Approach

Abstract

With the extensive use of Machine Learning (ML) in the biomedical field, there was an increasing need for Explainable Artificial Intelligence (XAI) to improve transparency and reveal complex hidden relationships between variables for medical practitioners, while meeting regulatory requirements. Feature Selection (FS) is widely used as a part of a biomedical ML pipeline to significantly reduce the number of variables while preserving as much information as possible. However, the choice of FS methods affects the entire pipeline including the final prediction explanations, whereas very few works investigate the relationship between FS and model explanations. Through a systematic workflow performed on 145 datasets and an illustration on medical data, the present work demonstrated the promising complementarity of two metrics based on explanations (using ranking and influence changes) in addition to accuracy and retention rate to select the most appropriate FS/ML models. Measuring how much explanations differ with/without FS are particularly promising for FS methods recommendation. While reliefF generally performs the best on average, the optimal choice may vary for each dataset. Positioning FS methods in a tridimensional space, integrating explanations-based metrics, accuracy and retention rate, would allow the user to choose the priorities to be given on each of the dimensions. In biomedical applica
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Dates and versions

hal-04258474 , version 1 (25-10-2023)

Identifiers

Cite

Haomiao Wang, Emmanuel Doumard, Chantal Soulé-Dupuy, Philippe Kemoun, Julien Aligon, et al.. Explanations as a New Metric for Feature Selection: A Systematic Approach. IEEE Journal of Biomedical and Health Informatics, 2023, 27 (8), pp.4131-4142. ⟨10.1109/JBHI.2023.3279340⟩. ⟨hal-04258474⟩
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