TSK fuzzy system fusion at sensitivity-ensemble-level for imbalanced data classification

Yuanpeng Zhang

doi:10.1016/j.inffus.2022.12.014

TSK fuzzy system fusion at sensitivity-ensemble-level for imbalanced data classification

2023-04-01

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AI

DOI：10.1016/j.inffus.2022.12.014

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Previous studies have shown that the performance of a classifier on imbalanced data heavily relies on infor-mative objects lying in borderline or overlapping areas. In this study, we adopt objective sensitivity to detect the most informative objects based on 0-order Takagi-Sugeno-Kang Fuzzy System (0-TSK-FS), and accordingly propose a novel TSKfuzzy system fusion framework at interpretable sensitivity-ensemble-level (ISE-TSK-FS) to achieve both promising classification performance and reasonable interpretability on imbalanced datasets. Specifically, we assume that a small perturbation of an input object can be interpreted as its neighboring future testing objects. Therefore, object sensitivity w.r.t 0-TSK-FS can be evaluated by the expectation of squared output differences between the input object and the objects in its neighborhood. Being guided by such object sensitivity, we partition the majority class using clustering into different blocks and then construct an ensemble under -sampling process to select the most informative objects from each block iteratively. Moreover, to avoid over -fitting issues in assembling, a self-paced factor is introduced to constrain the number of low-sensitive objects but still keep their skeleton contribution. Extensive experiments on 7 synthetic datasets, 30 UCI datasets and one real medical case demonstrate ISE-TSK-FS's promising performance and good interpretability on imbalanced data.

Keywords:

TSK fuzzy system

Imbalanced classification

Object sensitivity

Interpretability

Ensemble learning

TSK fuzzy system fusion at sensitivity-ensemble-level for imbalanced data classification

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