Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6939256 | Pattern Recognition | 2018 | 10 Pages |
Abstract
Feature selection remains a popular method for quantity reduction of attributes of high-dimensional data, to reduce computational costs in classifications. A new feature selection method based on the joint maximal information entropy between features and class (FS-JMIE) is proposed in this paper. Firstly, the joint maximal information entropy (JMIE) is defined to measure a feature subset. Next, a binary particle swarm optimization (BPSO) algorithm is introduced to search the optimal feature subset. Finally, classification is performed on UCI corpora to verify the performance of our proposed method compared to the traditional mutual information (MI) method, CHI method, as well as a binary version of particle swarm optimization-support vector machines (BPSO-SVMs) feature selection. Experiments show that FS-JMIE achieves an equal or better performance than MI, CHI, and BPSO-SVM. Further, FS-JMIE manifests relatively better robustness to the number of classes. Moreover, the method shows higher consistency and better time-efficiency than BPSO-SVM.
Related Topics
Physical Sciences and Engineering
Computer Science
Computer Vision and Pattern Recognition
Authors
Kangfeng Zheng, Xiujuan Wang,