Skip to main content

Research Repository

Advanced Search

Performance Optimization of a Fuzzy Entropy Based Feature Selection and Classification Framework

Shen, Zixiao; Chen, Xin; Garibaldi, Jon


Zixiao Shen

Associate Professor

Jon Garibaldi


© 2018 IEEE. In this paper, based on a fuzzy entropy feature selection framework, different methods have been implemented and compared to improve the key components of the framework. Those methods include the combinations of three ideal vector calculations, three maximal similarity classifiers and three fuzzy entropy functions. Different feature removal orders based on the fuzzy entropy values were also compared. The proposed method was evaluated on three publicly available biomedical datasets, including Wisconsin Breast Cancer(WBC), Wisconsin Diagnostic Breast Cancer(WDBC) and Parkinsons. From the experiments, we concluded the optimized combination of the ideal vector, similarity classifier and fuzzy entropy function for feature selection. The optimized framework was also compared with other six classical filter-based feature selection methods. The proposed method was ranked as one of the top performers together with the Correlation and ReliefF methods. The proposed method achieved classification accuracies of 96.97%, 94.85% and 78.23% for the WBC, WDBC and Parkinsons datasets respectively. More importantly, the proposed method achieved the most stable performance for all three datasets when the features being gradually removed. This indicates a better feature ranking performance than the other compared methods.

Conference Name Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018
Conference Location Miyazaki, Japan
Start Date Oct 7, 2018
End Date Oct 10, 2018
Acceptance Date Jun 1, 2018
Online Publication Date Jan 17, 2019
Publication Date Jan 16, 2019
Deposit Date Mar 27, 2019
Publicly Available Date Jan 15, 2020
Pages 1361-1367
ISBN 9781538666500
Public URL