论文标题
有监督的增强的软体子空间聚类（SESSC）用于TSK模糊分类器
Supervised Enhanced Soft Subspace Clustering (SESSC) for TSK Fuzzy Classifiers
论文作者
论文摘要
基于模糊的C均聚类算法经常用于Takagi-Sugeno-Kang（TSK）模糊分类器前提参数估计。一个规则是从每个集群初始化的。但是，这些聚类算法中的大多数都是无监督的，它们在培训数据中浪费了有价值的标签信息。本文提出了一种有监督的增强的软子空间聚类（SESSC）算法，该算法同时考虑了集群内部紧凑性，群集间隔和聚类中的标签信息。它可以有效地处理高维数据，单独用作分类器，或将其集成到TSK模糊分类器中以进一步提高其性能。来自各个应用程序域的九个UCI数据集的实验表明，基于SESSC的初始化优于其他聚类方法，尤其是当规则数量很少时。
Fuzzy c-means based clustering algorithms are frequently used for Takagi-Sugeno-Kang (TSK) fuzzy classifier antecedent parameter estimation. One rule is initialized from each cluster. However, most of these clustering algorithms are unsupervised, which waste valuable label information in the training data. This paper proposes a supervised enhanced soft subspace clustering (SESSC) algorithm, which considers simultaneously the within-cluster compactness, between-cluster separation, and label information in clustering. It can effectively deal with high-dimensional data, be used as a classifier alone, or be integrated into a TSK fuzzy classifier to further improve its performance. Experiments on nine UCI datasets from various application domains demonstrated that SESSC based initialization outperformed other clustering approaches, especially when the number of rules is small.
