Title: A new feature selection approach based on new multi-exhaustive search

Authors: Fatma Zohra Debba; Lynda Dib; Khaled Berrahil

Addresses: Department of Computer Science, Badji Mokhtar-Annaba University, Annaba, Algeria ' Department of Computer Science, Badji Mokhtar-Annaba University, Annaba, Algeria ' Center of Networks and Information Systems, Badji Mokhtar-Annaba University, Annaba, Algeria

Abstract: Feature Selection (FS) improves the classification model performance and comprehensibility by selecting informative feature subsets. It involves feature evaluators and search methods. Exhaustive search (ES) guarantees finding the optimal subset, but is computationally unfeasible, especially with large datasets. To overcome this drawback and take advantage of its optimality, this paper proposes a multi-exhaustive search (MES) to find the best subset in very low computational time. As a result, a new FS approach based on MES (FS-MES) is also proposed. The performance of FS-MES was evaluated using 11 benchmark datasets and compared with 13 FS approaches implemented in WEKA. A comparison with ES-based FS (FS-ES) demonstrated that FS-MES significantly reduced computation time and evaluations while maintaining competitive accuracy. Compared to the 12 other FS methods, FS-MES effectively outperformed all other FS methods in most of the datasets in terms of classification accuracy and number of selected features.

Keywords: feature selection; search methods; multi-exhaustive search; MES; exhaustive search; classification; machine learning.

DOI: 10.1504/IJIIDS.2026.152765

International Journal of Intelligent Information and Database Systems, 2026 Vol.18 No.2, pp.173 - 200

Received: 08 Dec 2023
Accepted: 28 Nov 2024
Published online: 10 Apr 2026 *

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