auto-xFS: An explanation-based feature selection tool for more meaningful and trustworthy machine learning models

Abstract

auto-xFS is a novel tool for feature selection (FS) based on a three-dimensional perspective encompassing feature retention rate, machine learning (ML) model performance, and explainability (XML). The application is designed to streamline the user’s workflow by autonomously hyperparameterizing FS techniques, ML models, and XML methods using a meta-learning approach. Our findings demonstrate that prioritizing FS that yields highly precise prediction explanations even at the expense of a slight reduction in model accuracy, can ensure more meaningful information for the user. auto-xFS is totally suited to FS in critical areas such as biomedicine where user confidence is crucial.