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A faster convergence and concise interpretability TSK fuzzy classifier deep-wide-based integrated learning

机译：更快的收敛性和简洁的解释性TSK模糊分类器基于深度宽的综合学习

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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.
机译：提出了分层TSK模糊系统，用于接近IF-DEL规则的指数增长，该规则命名为“模糊规则爆炸”。但是，对于TSK模糊系统的不稳定性，它无法获得更好的性能，使得等级TSK模糊系统受到影响从恶劣的解释性和慢融合以及太多的层次。为了获得更好的解决方案，本研究采用了更快的收敛性和简洁的解释性TSK模糊分类器深度范围的综合学习（FCCI-TSK），其具有广泛的结构来采用几个与堆叠的泛化原理的帮助，合奏单元学习的集合单元学习和最佳表现者将被拾取将其学习知识转移到下一层。通过负相关学习（NCL）集成了集合单元。FCCI-TSK调整输入下一层具有更好的指导，使得它可以加快收敛速度​​并减少层数。此外，引导指导，IT CA n实现更高的准确性和更好的解释性，结构更简单。本研究的贡献包括：（1）提高模糊分类器的性能，我们将NCL和堆叠的普遍原理混合在FCCI-TSK中; （2）为了克服“模糊规则爆炸”的现象，我们采用全面的综合学习和丢弃信息，以加速收敛，并在此期间获得简洁的解释性。与其他11个算法相比，12个UCI数据集的结果显示FCCI-TSK总体上具有最佳性能，还研究了FCCI-TSK的融合。（c）2019年Elsevier BV保留所有权利。

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