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

Shen, Zixiao; Chen, Xin; Garibaldi, Jon


Zixiao Shen

Assistant 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.

Start Date Oct 7, 2018
Publication Date Jan 16, 2019
Pages 1361-1367
ISBN 9781538666500
APA6 Citation Shen, Z., Chen, X., & Garibaldi, J. (2019). Performance Optimization of a Fuzzy Entropy Based Feature Selection and Classification Framework.