A novel multi-objective wrapper-based feature selection method using quantum-inspired and swarm intelligence techniques

Feature selection plays a pivotal role in machine learning, serving as a critical preprocessing step. Its impact extends beyond enhancing the classification capabilities of learning algorithms; it also enables the reduction of dataset dimensionality. Consequently, feature selection entails a multi-objective optimization problem, striving to minimize the number of features while maximizing classification accuracy. Surprisingly, there are only a few studies that approach feature selection from a multi-objective perspective, compared to the more prevalent single-objective viewpoint.Motivated by this gap, we present a novel multi-objective algorithm for tackling the feature selection problem. Our approach draws inspiration from quantum computing and combines the strengths of the Firefly Algorithm (FA) and the Particle Swarm Optimizer (PSO). Leveraging quantum computing enhances solution distribution, while the cooperative nature of FA and PSO facilitates effective exploration of the feature space. Additionally, we introduce two fixed-size external archives, dedicated to storing the best solutions. The archive sizes are controlled using the epsilon dominance relation. We evaluate the efficiency of our algorithm through an extensive comparison against both single and multi-objective feature selection algorithms that enjoy high regard in the field. Furthermore, we propose a high-performance detection system that harnesses our algorithm alongside three Convolutional Neural Network Algorithms. This system demonstrates its potential in accurately identifying COVID-19 disease from X-ray images. Our experimental results unequivocally establish the superiority of our proposed algorithm over its competitors. It consistently delivers feature subsets with a smaller number of features and achieves higher classification accuracy.