TS-Models from Evidential Clustering

We study how to derive a fuzzy rule-based classification model using the theoretical framework of belief functions. For this purpose we use the recently proposed Evidential c-means (ECM) to derive Takagi-Sugeno (TS) models solely from data. ECM allocates, for each object, a mass of belief to any subsets of possible clusters, which allows to gain a deeper insight in the data while being robust with respect to outliers. Some classification examples are discussed, which show the advantages and disadvantages of the proposed algorithm.

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