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Title: Imbalanced TSK fuzzy classifier by cross-class bayesian fuzzy clustering and imbalance learning
Authors: Gu, XQ
Chung, FL 
Ishibuchi, H
Wang, ST
Keywords: Bayesian fuzzy clustering
Imbalanced datasets
Parameter learning
Takagi-Sugeno-Kang (TSK) fuzzy classifier
Weighted average misclassification error
Issue Date: 2017
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE transactions on systems, man, and cybernetics. Systems, 2017, v. 47, no. 8, p. 2005-2020 How to cite?
Journal: IEEE transactions on systems, man, and cybernetics. Systems 
Abstract: In this paper, a novel construction algorithm called imbalanced Takagi-Sugeno-Kang fuzzy classifier (IB-TSK-FC) for the TSK fuzzy classifier is presented to improve the classification performance and rule-based interpretability for imbalanced datasets. IB-TSK-FC consists of two components: 1) a cross-class Bayesian fuzzy clustering algorithm (BF3C) and 2) an imbalance learning algorithm. In order to achieve high interpretability, BF3C is developed to determine an appropriate number of fuzzy rules and identify antecedent parameters of fuzzy rules from the perspective of the probabilistic model. In addition to inheriting the distinctive advantage of Bayesian fuzzy clustering that the number of clusters can be estimated in the framework of Bayesian inference, BF3C considers repulsion forces between cluster centers belonging to different classes, and uses an alternating iterative strategy to obtain more interpretable antecedent parameters for imbalanced datasets. In order to improve the classification performance for imbalanced datasets, an imbalance learning algorithm is derived to estimate consequent parameters of fuzzy rules on the basis of the weighted average misclassification error. Comprehensive experiments on synthetic and UCI datasets demonstrate the effectiveness of the proposed IB-TSK-FC algorithm.
ISSN: 2168-2216
EISSN: 2168-2232
DOI: 10.1109/TSMC.2016.2598270
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