A Feature Selection Method by using Chaotic Cuckoo Search Optimization Algorithm with Elitist Preservation and Uniform Mutation for Data Classification

Abstract

Feature selection is an essential step in the preprocessing of data in pattern recognition and data mining. Nowadays, the feature selection problem as an optimization problem can be solved with nature-inspired algorithm. In this paper, we propose an efficient feature selection method based on the cuckoo search algorithm called CBCSEM. The proposed method avoids the premature convergence of traditional methods and the tendency to fall into local optima, and this efficient method is attributed to three aspects. Firstly, the chaotic map increases the diversity of the initialization of the algorithm and lays the foundation for its convergence. Then, the proposed two-population elite preservation strategy can find the attractive one of each generation and preserve it. Finally, Lévy flight is developed to update the position of a cuckoo, and the proposed uniform mutation strategy avoids the trouble that the search space is too large for the convergence of the algorithm due to Lévy flight and improves the algorithm exploitation ability. The experimental results on several real UCI datasets show that the proposed method is competitive in comparison with other feature selection algorithms.