Biologically Plausible Fuzzy-Knowledge-Out and Its Induced Wide Learning of Interpretable TSK Fuzzy Classifiers

As an alternative to existing construction methods of Takagi-Sugeno-Kang (TSK) fuzzy classifiers, this paper presents a novel design methodology formulated by a new concept called fuzzy-knowledge-out and its induced wide learning way. Analogous to the "dropout" concept in deep learning, th...

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Bibliographic Details
Published in:IEEE transactions on fuzzy systems 2020-07, Vol.28 (7), p.1276-1290
Main Authors: Qin, Bin, Chung, Fu-lai, Wang, Shitong
Format: Article
Language:English
Subjects:
Biological neural networks
Classification
Classifiers
Co-adaption
Construction methods
Deep learning
Design engineering
Design methodology
fuzzy classifier
Fuzzy sets
Fuzzy systems
fuzzy-knowledge-out
Knowledge
Linguistics
Machine learning
Neural networks
overfitting
Takagi–Sugeno–Kang (TSK)
Training
wide learning
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Summary:As an alternative to existing construction methods of Takagi-Sugeno-Kang (TSK) fuzzy classifiers, this paper presents a novel design methodology formulated by a new concept called fuzzy-knowledge-out and its induced wide learning way. Analogous to the "dropout" concept in deep learning, the concept of fuzzy-knowledge-out in TSK fuzzy classifiers is motivated by the firing pattern of knowledge in biological neural networks. Our theoretical analysis reveals that a fuzzy classifier built after fuzzy-knowledge-out from a complete set of highly interpretable fuzzy rules is distinctive in generalization and coadaption avoidance. As such, an ensemble called wide learning based TSK (WL-TSK), of highly interpretable zero-order TSK fuzzy subclassifiers constructed quickly by means of fuzzy-knowledge-out operations in a wide learning manner is proposed to achieve enhanced classification performance and high interpretability. With the use of the proposed halving or averaging operations, WL-TSK essentially behaves like only one zero-order TSK fuzzy classifier. Thus, the proposed method can be considered as a new design methodology of TSK fuzzy classifiers. Our experimental results on 15 datasets indicate the effectiveness of WL-TSK in terms of both enhanced classification performance and high interpretability.
ISSN:1063-6706
1941-0034
DOI:10.1109/TFUZZ.2019.2907497