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Authors: Artur Ferreira 1 ; 2 ; 3 and Mário Figueiredo 3 ; 2 ; 1

Affiliations: 1 ISEL, Instituto Superior de Engenharia de Lisboa, Instituto Politécnico de Lisboa, Portugal ; 2 Instituto de Telecomunicações, Lisboa, Portugal ; 3 IST, Instituto Superior Técnico, Universidade de Lisboa, Portugal

Keyword(s): Cancer Detection, Classification, k-Fold Data Split, Explainability, Feature Selection, Leave-One-Out.

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 dimens ionality 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. (More)

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Paper citation in several formats:
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 - ICPRAM; ISBN 978-989-758-626-2; ISSN 2184-4313, SciTePress, pages 458-465. DOI: 10.5220/0011744400003411

@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 - ICPRAM},
year={2023},
pages={458-465},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011744400003411},
isbn={978-989-758-626-2},
issn={2184-4313},
}

TY - CONF

JO - Proceedings of the 12th International Conference on Pattern Recognition Applications and Methods - ICPRAM
TI - Leveraging Explainability with K-Fold Feature Selection
SN - 978-989-758-626-2
IS - 2184-4313
AU - Ferreira, A.
AU - Figueiredo, M.
PY - 2023
SP - 458
EP - 465
DO - 10.5220/0011744400003411
PB - SciTePress