Imbalanced TSK Fuzzy Classifier by Cross-Class Bayesian Fuzzy Clustering and Imbalance Learning

Xiaoqing Gu, Fu Lai Korris Chung, Hisao Ishibuchi, Shitong Wang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

56 Citations (Scopus)


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.
Original languageEnglish
Article number7549102
Pages (from-to)2005-2020
Number of pages16
JournalIEEE Transactions on Systems, Man, and Cybernetics: Systems
Issue number8
Publication statusPublished - 1 Aug 2017


  • Bayesian fuzzy clustering
  • imbalanced datasets
  • parameter learning
  • Takagi-Sugeno-Kang (TSK) fuzzy classifier
  • weighted average misclassification error

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Human-Computer Interaction
  • Computer Science Applications
  • Electrical and Electronic Engineering


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