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Title: Deep TSK fuzzy classifier with stacked generalization and triplely concise interpretability guarantee for large data
Authors: Zhou, T
Chung, FL 
Wang, S
Keywords: Deep learning Takagi-Sugeno-Kang (TSK)
Fuzzy classifier
Large datastacked generalization
Least learning machine (LLM)
Issue Date: 2017
Publisher: Institute of Electrical and Electronics Engineers Inc.
Source: IEEE transactions on fuzzy systems, 2017, v. 25, no. 5, 7555341, p. 1207-1221 How to cite?
Journal: IEEE transactions on fuzzy systems 
Abstract: Although Takagi-Sugeno-Kang (TSK) fuzzy classifier has been applied to a wide range of practical scenarios, how to enhance its classification accuracy and interpretability simultaneously is still a challenging task. In this paper, based on the powerful stacked generalization principle, a deep TSK fuzzy classifier (D-TSK-FC) is proposed to achieve the enhanced classification accuracy and triplely concise interpretability for fuzzy rules. D-TSK-FC consists of base-building units. Just like the existing popular deep learning, D-TSK-FC can be built in a layer-by-layer way. In terms of the stacked generalization principle, the training set plus random shifts obtained from random projections of prediction results of current base-building unit are presented as the input of the next base-building unit. The hidden layer in each base-building unit of D-TSK-FC is represented by triplely concise interpretable fuzzy rules in the sense of randomly selected features with the fixed five fuzzy partitions, random rule combinations, and the same input space kept in every base-building unit of D-TSK-FC. The output layer of each base-building unit can be learnt quickly by least learning machine (LLM). Besides, benefiting from LLM, D-TSK-FC's deep learning can be well scaled up for large datasets. Our extensive experimental results witness the power of the proposed deep TSK fuzzy classifier.
ISSN: 1063-6706
EISSN: 1941-0034
DOI: 10.1109/TFUZZ.2016.2604003
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