ITM Web Conf.
Volume 43, 2022The International Conference on Artificial Intelligence and Engineering 2022 (ICAIE’2022)
|Number of page(s)||6|
|Published online||14 March 2022|
Feature Selection with a Backtracking Search Optimization Algorithm
1 Dept. of Cultural Technology and Communication University of the Aegean Mytilene, Greece
2 Dept. of Cultural Technology and Communication University of the Aegean Mytilene, Greece
Feature selection carries significance in the outcome of any classification or regression task. Exercising evolutionary computation algorithms in feature selection has led to the construction of efficient discrete optimization algorithms. In this paper, a modified backtracking search algorithm is employed to perform wrapper-based feature selection, where two modifications of the standard backtracking search algorithm are adopted. The first one concentrates on utilizing a particle ranking operator regarding the current population. The second one focuses on removing the case of using a single particle on the mutation process. Then, the implementation of the above algorithm in feature selection is carried out in terms of two general frameworks, which originally were developed for the particle swarm optimization. The first framework is based on the binary and the second on the set-based particle swarm optimization. The experimental analysis shows that the above variants of the backtracking search algorithm perform equally well on the classification of several datasets.
Key words: Index Terms / Backtracking search optimization / feature selection / binary search / set-based search
© The Authors, published by EDP Sciences, 2022
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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