Supervised Enhanced Soft Subspace Clustering (SESSC) for TSK Fuzzy Classifiers

Abstract

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 highdimensional 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.

Curse of Dimensionality for TSK Fuzzy Neural Networks: Explanation and Solutions

“…As indicated by (7) and (8), increasing the scale of σ also increases the value of Z r to avoid saturation. Similar tricks have already been used for training TSK models with fuzzy clustering algorithms, such as FCM [14], ESSC [16] and SESSC [6]. The parameter σ is computed by:…”

“…Fuzzy clustering [4]- [6] and evolutionary algorithms [7], [8] have been used to determine the parameters of TSK fuzzy systems on small datasets. However, their computational cost may be too high for big data.…”

“…Traditional optimization algorithms for TSK fuzzy systems use grid partition to partition the input space into different fuzzy regions, whose number grows exponentially with the input dimensionality. A more popular and flexible way is clustering-based partition, e.g., fuzzy c-means (FCM) [14], EWFCM [15], ESSC [4], [16] and SESSC [6], in which the fuzzy sets in different rules are independent and optimized separately.…”