A Weight-Featured and Data-Distribution-Based Fuzzy Pattern Classification Approach

Most research proposed so far for fuzzy pattern classification has not considered the characteristic of data distribution in a given data set during the process of clustering. This paper proposes an approach that can appropriately cluster a given data set automatically based on data distribution of a given data set without the need of specifying the number of resultant clusters and setting up subjective parameters. Some special data distributions, such as stripe- or belt-shaped distributions, can therefore be nicely clustered for better pattern classification. Statistical concept is applied to define weights of pattern features so that the weight of a pattern feature is proportional to the contribution the feature can provide to the task of pattern classification. The proposed weight definition not only reduces the dimensionality of feature space so as to speed up the classification process but also increases the accuracy rate of classification result. The experiments in this paper demonstrate the proposed method has fewer fuzzy rules and better classification accuracy than other related methods.