A Novel Weighted Combination Method for Feature Selection using Fuzzy Sets

Shen, Zixiao; Chen, Xin; Garibaldi, Jonathan M.

Abstract

In this paper, we propose a novel weighted combination feature selection method using bootstrap and fuzzy sets. The proposed method mainly consists of three processes, including fuzzy sets generation using bootstrap, weighted combination of fuzzy sets and feature ranking based on defuzzification. We implemented the proposed method by combining four state-of-the-art feature selection methods and evaluated the performance based on three publicly available biomedical datasets using fivefold cross validation. Based on the feature selection results, our proposed method produced comparable (if not better) classification accuracies to the best of the individual feature selection methods for all evaluated datasets. More importantly, we also applied standard deviation and Pearson's correlation to measure the stability of the methods. Remarkably, our combination method achieved significantly higher stability than the four individual methods when variations and size reductions were introduced to the datasets.

Citation

Shen, Z., Chen, X., & Garibaldi, J. M. (2019). A Novel Weighted Combination Method for Feature Selection using Fuzzy Sets. In 2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)https://doi.org/10.1109/fuzz-ieee.2019.8858890

• Conference Name: 2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)
• Start Date: Jun 23, 2019
• End Date: Jun 26, 2019
• Acceptance Date: Mar 4, 2019
• Online Publication Date: Oct 10, 2019
• Publication Date: 2019-06
• Deposit Date: Nov 5, 2019
• Publisher: Institute of Electrical and Electronics Engineers
• Book Title: 2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)
• ISBN: 9781538617281
• DOI: https://doi.org/10.1109/fuzz-ieee.2019.8858890
• Public URL: https://nottingham-repository.worktribe.com/output/3062771
• Publisher URL: https://ieeexplore.ieee.org/document/8858890
• Additional Information: © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.