Explanations as a New Metric for Feature Selection: A Systematic Approach - Archive ouverte HAL Accéder directement au contenu
Article Dans Une Revue IEEE Journal of Biomedical and Health Informatics Année : 2023

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

Résumé

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 applications, where each medical condition may have its own preferences, this framework will make it possible to offer the healthcare professional the appropriate FS technique, to select the variables that have an important explainable impact, even if this comes at the expense of a limited drop of accuracy.
Fichier principal
Vignette du fichier
XAI_based_Feature_Selection_Benchmark.pdf (2.07 Mo) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)

Dates et versions

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

Identifiants

Citer

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⟩
90 Consultations
56 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More