A Hybrid Method of Sine Cosine Algorithm and Differential Evolution for Feature Selection
The feature selection is an important step to improve the performance of classifier through reducing the dimension of the dataset, so the time complexity and space complexity are reduced. There are several feature selection methods are used the swarm techniques to determine the suitable subset of features. The sine cosine algorithm (SCA) is one of the recent swarm techniques that used as global optimization method to solve the feature selection, however, it can be getting stuck in local optima. In order to solve this problem, the differential evolution operators are used as local search method which helps the SCA to skip the local point. The proposed method is compared with other three algorithms to select the subset of features used eight UCI datasets. The experiments results showed that the proposed method provided better results than other methods in terms of performance measures and statistical test.
KeywordsFeature selection (FS) Sine Cosine Algorithm (SCA) Differential evolution (DE) Metaheuristic (MH)
This work was in part supported by national key Research & Development Program of China (No. 2016YFD0101903), Nature Science Foundation of Hubei Province (Grant No. 2015CFA059), Science & Technology Pillar Program of Hubei Province (Grant No. 2014BAA146), Science & Technology Cooperation Program of Henan Province (No. 152106000048) and Hubei Collaborative Innovation Center of Basic Education Information technology Services.
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