A New Approach for Feature Subset Selection using Quantum Inspired Owl Search Algorithm

Feature subset selection is the approach of selecting the optimal feature subset for the classification task by removing irrelevant and redundant features. However, searching for the optimal feature subset is challenging due to its inherent exponential time complexity. To address the problem, metaheuristics are frequently used for finding the sub-optimal solution in a reasonable time constraint. In this paper, a Quantum Inspired Owl Search Algorithm (QIOSA) for feature subset selection is proposed. In this method, features are represented as quantum superposition states, and quantum rotation gate is used to accelerate the search towards an optimal set of features. Simulation experiments have done to evaluate the efficiency of the proposed approach compared to Binary Owl Search Algorithm (BOSA) proposed earlier by authors and other population-based feature selection techniques, including Binary Genetic Algorithm (BGA) and Binary Particle Swarm optimization (BPSO) with twelve publicly available benchmark datasets. The experimental results show that QIOSA improves classification accuracy and effectively reduces the number of features compared to other metaheuristic algorithms.