Authors:
Artur Ferreira
1
;
2
and
Mário A. T. Figueiredo
3
;
2
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):
Explainability, Feature Selection, Filter, Interpretability, Intersection of Filters, K-Fold Feature Selection, Union of Filters.
Abstract:
Feature selection (FS) is a vast research topic with many techniques proposed over the years. FS techniques may bring many benefits to machine learning algorithms. The combination of FS techniques usually improves the results as compared to the use of one single technique. Recently, the concepts of explainability and interpretability have been proposed in the explainable artificial intelligence (XAI) framework. The recently proposed k-fold feature selection (KFFS) algorithm provides dimensionality reduction and simultaneously yields an output suitable for explainability purposes. In this paper, we extend the KFFS algorithm by performing union and intersection of the individual feature subspaces of two and three feature selection filters. Our experiments performed on 20 datasets show that the union of the feature subsets typically attains better results than the use of individual filters. The intersection also attains adequate results, yielding human manageable (e.g., small) subsets o
f features, allowing for explainability and interpretability on medical domain data.
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