Evolutionary Feature Selection Method via A Chaotic Binary Dragonfly Algorithm - Liu, Zhao; Wang, Aimin; Sun, Geng; Li, Jiahui; Bao, Haiming; Li, Hongjuan (Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics, 2022)
Feature selection aims at reducing the number of attributes while achieving a high classification accuracy in machine learning. In this paper, we design a fitness function to jointly reduce the number of the selected features and enhance the accuracy. Then, we propose a chaotic binary dragonfly algorithm (CBDA) with several improved factors on the conventional dragonfly algorithm (DA) for developing a wrapper-based feature selection method to solve the fitness function. Specifically, the CBDA introduces three improved factors that are the chaotic map, evolutionary population dynamics mechanism and binarization strategy to make the algorithm more suitable for the problem. Experiments are conducted to evaluate the performance of the proposed CBDA on 24 well-known data sets from the UCI repository, and the results demonstrate that the proposed CBDA outperforms other comparative algorithms on the majority of the tested data sets.