In this paper, the Takagi-Sugeno-Kang (TSK) fuzzy classifier is casted into the Bayesian
inference framework and a new fuzzy classifier called Bayesian TSK fuzzy classifier (B-TSK …

For many application scenarios where raw and even multidomain training data can be easily
collected, and at the same time, validation data (as ground-truth data) are available, it …

In traditional hierarchical fuzzy classifiers, there exist some main problems. These issues
include the output of the previous training layer influencing with the input of the next layers …

While wide fuzzy classifiers which combine several TSK fuzzy sub-classifiers with some
aggregation strategy like weighting have been widely developed to achieve good …

Takagi–Sugeno–Kang (TSK) fuzzy inference has been widely used in approximating
uncertain nonlinear systems because of its high interpretability and precision. However, TSK …

In many practical applications of classifiers, not only high accuracy but also high
interpretability is required. Among a wide variety of existing classifiers, Takagi–Sugeno …

In this paper, a novel Bayesian Takagi-Sugeno-Kang (BTSK) fuzzy model and its joint
learning method BTSK-JL of structure identification and parameter estimation are proposed …

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 …

In this paper, we propose a blockwise combination of interpretable Takagi-Sugeno-Kang
(TSK) fuzzy classifiers to simultaneously achieve high accuracy and concise interpretability …

In this paper, a new stacked-structure-based hierarchical Takagi-Sugeno-Kang (TSK) fuzzy
classifier called SHFA-TSK-FC with both promising performance and high interpretability is …