Leveraging Explainability with K-Fold Feature Selection

Artur Ferreira, Artur Ferreira, Artur Ferreira, Mário Figueiredo, Mário Figueiredo, Mário Figueiredo

2023

Abstract

Learning with high-dimensional (HD) data poses many challenges, since the large number of features often yields redundancy and irrelevance issues, which may decrease the performance of machine learning (ML) methods. Often, when learning with HD data, one resorts to feature selection (FS) approaches to avoid the curse of dimensionality. The use of FS may improve the results, but its use by itself does not lead to explainability, in the sense of identifying the small subset of core features that most influence the prediction of the ML model, which can still be seen as a black-box. In this paper, we propose k-fold feature selection (KFFS), which is a FS approach to shed some light into that black-box, by resorting to the k-fold data partition procedure and one generic unsupervised or supervised FS filter. KFFS finds small and decisive subsets of features for a classification task, at the expense of increased computation time. On HD data, KFFS finds small subsets of features, with dimensionality small enough to be analyzed by human experts (e.g, a medical doctor in a cancer detection problem). It also provides classification models with lower error rate and fewer features than those provided by the use of the individual supervised FS filter.

Paper Citation

in Harvard Style

Ferreira A. and Figueiredo M. (2023). Leveraging Explainability with K-Fold Feature Selection. In Proceedings of the 12th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-626-2, pages 458-465. DOI: 10.5220/0011744400003411

in Bibtex Style

@conference{icpram23,
author={Artur Ferreira and Mário Figueiredo},
title={Leveraging Explainability with K-Fold Feature Selection},
booktitle={Proceedings of the 12th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2023},
pages={458-465},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011744400003411},
isbn={978-989-758-626-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 12th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Leveraging Explainability with K-Fold Feature Selection
SN - 978-989-758-626-2
AU - Ferreira A.
AU - Figueiredo M.
PY - 2023
SP - 458
EP - 465
DO - 10.5220/0011744400003411