A novel feature selection approach based on constrained eigenvalues optimization

It is often tricky in real-life classification applications to select model features that would ensure an adequate sample classification, given a large number of candidate features. Our main contribution is threefold: (1) Evaluate the relevance and redundancy of feature. (2) Define the feature selection problem as eigenvalue computation problem with linear constraint. (3) Select the best features in an efficient way. We considered 20 UCI benchmark datasets to validate and test our approach. The results were compared with those obtained using one of the more widely used approaches, namely mRMR, the conventional fea-tures and two moderns state-of-the-art approaches. The experimental results revealed that our approach could improve the classification task, using only 20% of the conventional features.(c) 2021 The Authors. Published by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Automated summary

In real-life classification applications, it can be challenging to select model features that adequately classify samples from a large number of candidates. This article's main contributions include evaluating the relevance and redundancy of features, defining the feature selection problem as an eigenvalue computation problem with a linear constraint, and efficiently selecting the best features. The approach was tested on 20 UCI benchmark datasets and compared with other widely used and state-of-the-art approaches. The experimental results showed that our approach improved the classification task by using only 20% of the conventional features.