摘要:虽然Takagi-Sugeno-Kang （TSK）模糊分类器在一些重要场合已经取得了广泛应用，但如何提高其分类性能和增强其可解释性，仍然是目前的研究热点.提出一种随机划分与组合特征且规则具有高可解释性的深度TSK模糊分类器（RCC-DTSK-C），但和其他分类器构造不同的是：（1） RCC-DTSK-C由很多基训练单元构成，这些基训练单元可以被独立训练；（2）每一个基训练单元的隐含层通过模糊规则的可解释性来表达，而这些模糊规则又是通过随机划分、随机组合来进行特征选择的；（3）基于栈式结构理论，源数据集作为相同的输入空间被映射到每一个独立的基训练单元中，这样就有效地保证了源数据的所有特征在每一个独立的训练单元中都得以保留.实验结果表明，RCC-DTSK-C具有良好的分类性能和可解释性.



Abstract:Although Takagi-Sugeno-Kang (TSK) is widely used in practically every profession, how to enhance its classification accuracy and interpretability is still a research focus. In this study, a deep TSK fuzzy classifier is proposed. This classifier (i.e., RCC-DTSK-C) can randomly select features and combine features and own triplely concise interpretability for fuzzy rules. There are several other varieties of RCC-DTSK-C such as reasonable structure for rule representation, namely, (1) the proposed RCC-DTSK-C consists of many base-training units and each base-training unit can be trained independently. According to the principle of stacked generalization, the input of the next base-training unit consists of the training set and random result obtained from random projections about prediction results of current base-training unit. (2) In RCC-DTSK-C, the hidden layer of each base-training unit is represented by triplely concise interpretable fuzzy rules which are in the sense of randomly selected features. These features are selected by dividing into the not-fixed several fuzzy partitions and randomly combining rules and keeping the same input space in every base-training unit. (3) The source data set is mapped into each of the independent base-training units as the same input space, which effectively ensures that all the features of the source data are preserved in each separate training unit. The extensive experimental results show RCC-DTSK-C can achieve the enhanced classification performance and triplely concise interpretability for fuzzy rules.



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