A Hybrid Improved Dragonfly Algorithm for Feature Selection

Xue-Ting Cui, Ying Li, Jia-Hao Fan, Tan Wang, Yue-Feng Zheng

Abstract: Feature selection, which eliminates irrelevant and redundant features, is one of the most efficient classification methods. However, searching for an optimal subset from the original set is still a challenging problem. This paper proposes a novel feature selection algorithm named hybrid improved dragonfly algorithm (HIDA) which combines the advantages of both mRMR and improved dragonfly algorithm (IDA) in order to generate promising candidate subset and achieve higher classification accuracy rate. Firstly, to generate promising subset, features with small weight have chance to be selected into candidate subset with a small probability in mRMR. Secondly, to balance the exploitation and exploration capabilities of IDA, dynamic swarming factors are proposed to balance global and local capability. Lastly, to enhance the exploitation capability of IDA, quantum local optimum and global optimum are introduced in the position updating mechanism. The performance of HIDA is investigated on ten gene expression datasets and eight UCI data sets from the UCI Machine Learning Data Repository. Results show that the performance of HIDA is superior to BBA, BDA, CDA, LBPSO, MPMDWOA and MSMCCS.