In this paper, we propose a blockwise combination of interpretable Takagi–Sugeno–Kang (TSK) fuzzy classifiers to simultaneously achieve high accuracy and concise interpretability. As a special hierarchical fuzzy classifier, the proposed classifier is built in a stacked block-by-block way. Each base building block consists of multiple zero-order TSK fuzzy classifiers, which are simultaneously trained in an analytical manner by using negative correlation learning to enhance the generalization ability of the base building block. For utilizing the stacked generalization principle, a random projection of the outputs from the current base building block is presented to the next base building block together with the current training sample in order to enhance the generalization ability of our hierarchical fuzzy classifier. The purpose of such a special hierarchical structure is that all base building blocks can be trained in the same input–output space with the current training sample and the randomly projected output from the previous building block. In the input layer, the target output for the current training sample is used instead of the randomly projected output from the previous building block. Each TSK fuzzy classifier in base building blocks consists of interpretable TSK fuzzy rules, which are generated by randomly selecting input features and randomly assigning an antecedent fuzzy subset from a fixed fuzzy partition to each of the selected input features. Merits of the proposed classifier are demonstrated through comparative studies on benchmark datasets.

中文翻译：

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通过负相关学习的可解释 TSK 模糊分类器的堆叠块状组合

在本文中，我们提出了可解释的 Takagi-Sugeno-Kang (TSK) 模糊分类器的分块组合，以同时实现高精度和简洁的可解释性。作为一种特殊的分层模糊分类器，所提出的分类器以逐块堆叠的方式构建。每个基础积木由多个零阶TSK模糊分类器组成，通过使用负相关学习以分析方式同时训练，以增强基础积木的泛化能力。为了利用堆叠泛化原理，将当前基本构建块的输出与当前训练样本一起呈现到下一个基本构建块的随机投影，以增强我们的分层模糊分类器的泛化能力。这种特殊层次结构的目的是所有基础构建块都可以在相同的输入-输出空间中与当前训练样本和前一个构建块的随机投影输出一起训练。在输入层中，使用当前训练样本的目标输出而不是前一个构建块的随机投影输出。基本构建块中的每个 TSK 模糊分类器由可解释的 TSK 模糊规则组成，这些规则是通过随机选择输入特征并将来自固定模糊分区的先行模糊子集随机分配给每个选定的输入特征而生成的。通过对基准数据集的比较研究，证明了所提出的分类器的优点。