A cooperative swarm intelligence algorithm based on quantum-inspired and rough sets for feature selection

Quantum computation ensures a good trade-off between the exploration and the exploitation of the search space while the combination of the FA and PSO enables an effective exploration of all the possible feature subsets. We use rough set theory to assess the relevance of the potential generated feature subsets. We tested the proposed algorithm on eleven UCI datasets and compared with a deterministic rough set reduction algorithms and other swarm intelligence algorithms. The experiment results show clearly that our algorithm provides a better rate of feature reduction and a high accuracy classification.