An incremental zero-order TSK fuzzy classifier called TSK-IFCIRLS0 was proposed based on iteratively reweighted least squares optimization algorithm in order to circumvent the drawback that traditional non-fuzzy classifiers had no any interpretability and that fuzzy classifiers could not always be feasible for many datasets with satisfied classification performance. The incremental fuzzy clustering algorithm IFCM(c+p) for large-scale datasets was used to quickly train antecedent parameters of fuzzy rules by clustering and using Gauss function to map the clustering results into fuzzy subspace. The iteratively reweighted least squares optimization algorithm was used to learn consequent parameters of fuzzy rules. The robust version called TSK-IFCPHub0 was developed based on pseudo-Huber loss function with the purpose of improving anti-noise ability of TSK-IFCIRLS0. The proposed fuzzy classifiers were experimentally compared with conventional fuzzy classifier FCPM-IRLS, RBF neural network and ANFIS. Results indicated the power of the proposed fuzzy classifiers on interpretability, classification performance and scalability. The strong robust capability of TSK-IFCPHub0 was verified by the experimental results.
Received: 31 August 2016
Published: 27 September 2017
LI Tao, WANG Shi-tong. Incremental zero-order TSK fuzzy classifier and its robust version. JOURNAL OF ZHEJIANG UNIVERSITY (ENGINEERING SCIENCE), 2017, 51(10): 1901-1911.
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