关键词: TSK模糊系统, 单调分类, 单调约束, 分类性能, 可解释性

Abstract: TSK (Takagi-Sugeno-Kang) fuzzy system has been widely used in regression, classification and decision-making, and has showed a great precision and interpretability. But for the modeling scene with monotonic data, the modeling effect of TSK fuzzy system is not ideal enough. In order to overcome the above challenge, this paper proposes a novel monotonic TSK fuzzy system (MC-TSK). The MC-TSK satisfies monotonicity by adding monotonic constraints to the original TSK fuzzy system model. MC-TSK does not require that all the monotonic relationships between features and the decision attribute are consistent, which relaxes the assumption that consistent monotonicity is used in the most existing methods for dealing with monotonic classification problems. The extensive experimental results show that MC-TSK has better classification performance and maintains the characteristics of interpretability.

Key words: TSK fuzzy system, monotonic classification, monotonic constraints, classification performance, interpretability