关键词: 区间二型模糊系统, TSK系统, 子空间, 分类, 可解释性

Abstract: People tend to adopt a few representative features to describe a rule, ignoring very minor, redundant information. The classical interval type-2 TSK (Takagi-Sugeno-Kang) fuzzy system adopts full data feature space at the antecedent and consequent of each rule. For high-dimensional data, it is easy to increase the complexity and reduce the interpretability. This paper proposes the interval type-2 fuzzy subspace zero-order TSK system for this problem. It uses fuzzy subspace clustering and grid partition to generate sparse and structured rule centers at the antecedent, and simplifies the consequent to be zero-order. So an interval type-2 fuzzy system using semantic more concise rules can be built. The experimental results on synthetic datasets and real datasets show that the proposed method owns good classification performance with better interpretability.

Key words:  interval type-2 fuzzy system, TSK system, subspace, classification, interpretability