Random Fuzzy Clustering Granular Hyperplane Classifier

Abstract

Granular computing is a method of studying human intelligent information processing, which has advantage of knowledge discovery. In this paper, we convert a classification problem of sample space into a classification problem of fuzzy clustering granular space and propose a random fuzzy clustering granular hyperplane classifier (RFCGHC) from the perspective of granular computing. Most classifiers are only used to process numerical data, RFCGHC can process not only non-numerical data, such as information granules, but also numerical data. The classic granulation method is generally serial granulation, which has high time complexity. We design a parallel distributed granulation method to enhance efficiency. First, a clustering algorithm with adaptive cluster center number is proposed, where the ratio of standard deviation between categories and standard deviation within categories is as evaluation criterion. The clusters and the optimal amount of cluster centers can be achieved by the method. On the basis of these, sample set can be divided into many subsets and each sample can be granulated by these cluster centers. Then, a fuzzy clustering granular space can be formed, where fuzzy clustering granules, fuzzy clustering granular vectors, and their operators can be defined. In order to get the optimal hyperplane in the fuzzy clustering granular space to classify these samples, we design a loss function and evaluate each category with probability by fuzzy clustering granular hyperplane. In solving the loss function, genetic algorithm based on fuzzy clustering granules is adopted. Experimental results and theoretical analysis show that RFCGHC has good performance.