A Broad TSK Fuzzy Classifier with a Simplified Set of Fuzzy Rules for Class-Imbalanced Learning

Jinghong Zhang, Yingying Li, Bowen Liu, Hao Chen, Jie Zhou, Hualong Yu and Bin Qin ()
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Jinghong Zhang: School of Computer, Jiangsu University of Science & Technology, Zhenjiang 212100, China
Yingying Li: School of Computer, Jiangsu University of Science & Technology, Zhenjiang 212100, China
Bowen Liu: School of Computer, Jiangsu University of Science & Technology, Zhenjiang 212100, China
Hao Chen: School of Computer, Jiangsu University of Science & Technology, Zhenjiang 212100, China
Jie Zhou: Department of Computer Science & Engineering, Shaoxing University, Shaoxing 312000, China
Hualong Yu: School of Computer, Jiangsu University of Science & Technology, Zhenjiang 212100, China
Bin Qin: School of Computer, Jiangsu University of Science & Technology, Zhenjiang 212100, China

Mathematics, 2023, vol. 11, issue 20, 1-30

Abstract: With the expansion of data scale and diversity, the issue of class imbalance has become increasingly salient. The current methods, including oversampling and under-sampling, exhibit limitations in handling complex data, leading to overfitting, loss of critical information, and insufficient interpretability. In response to these challenges, we propose a broad TSK fuzzy classifier with a simplified set of fuzzy rules (B-TSK-FC) that deals with classification tasks with class-imbalanced data. Firstly, we select and optimize fuzzy rules based on their adaptability to different complex data to simplify the fuzzy rules and therefore improve the interpretability of the TSK fuzzy sub-classifiers. Secondly, the fuzzy rules are weighted to protect the information demonstrated by minority classes, thereby improving the classification performance on class-imbalanced datasets. Finally, a novel loss function is designed to derive the weights for each TSK fuzzy sub-classifier. The experimental results on fifteen benchmark datasets demonstrate that B-TSK-FC is superior to the comparative methods from the aspects of classification performance and interpretability in the scenario of class imbalance.

Keywords: TSK fuzzy classifiers; fuzzy rule reduction; weighted fuzzy rule; broad ensemble learning; class-imbalanced learning (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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