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A faster convergence and concise interpretability TSK fuzzy classifier deep-wide-based integrated learning
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DOI：10.1016/j.asoc.2019.105825.png)
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Abstract
En 中文
Hierarchical TSK fuzzy system was proposed to approach the exponential growth of IF-THEN rules which named fuzzy rule explosion''. However, it could not get better performance in few layers for instability of TSK fuzzy system, such that hierarchical TSK fuzzy system suffers from bad interpretability and slow convergence along with too much layers. To get a better solution, this study employs a faster convergence and concise interpretability TSK fuzzy classifier deep-wide-based integrated learning (FCCI-TSK) which has a wide structure to adopt several ensemble units learning in a meantime, and the best performer will be picked up to transfer its learning knowledge to next layer with the help of stacked generalization principle. The ensemble units are integrated by negative correlation learning (NCL). FCCI-TSK adjusts the input of the next layer with a better guidance such that it can quicken the speed of convergence and reduce the number of layers. Besides, leading with guidance, it can achieve higher accuracy and better interpretability with more simple structure. The contributions of this study include: (1) To enhance the performance of fuzzy classifier, we mix NCL and stacked generalization principle together in FCCI-TSK; (2) To overcome the phenomenon of fuzzy rule explosion'', we adopt deep-wide integrated learning and information discarding to accelerate convergence and obtain concise interpretability in the meantime. Comparing with other 11 algorithms, the results on twelve UCI datasets show that FCCI-TSK has the best performance overall and the convergence of FCCI-TSK is also examined. (C) 2019 Elsevier B.V. All rights reserved.
Keywords:
Interpretability
Negative correlation learning
Stacked generalization principle
TSK fuzzy classifier
Convergence performance
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