Granular ball computing classifiers for efficient, scalable and robust learning

Abstract

Granular computing is an efficient and scalable computing method. Most of the existing granular computing-based classifiers treat the granules as a preliminary feature procession method, without revising the mathematical model and improving the main performance of the classifiers themselves. So far, only few methods, such as the G-svm and WLMSVM, have been combined with specific classifiers. Because of the complete symmetry of the ball and its simple mathematical expression, it is relatively easy to be combined with the other classifiers’ mathematical models. Therefore, this paper uses a ball to represent the grain, namely the granular ball, and not only the granular balls’ labels but also the distance between a pair of balls is defined. Based on that, this paper attempts to propose a new granular classifier framework by replacing the point inputs with the granular balls. We derive the basic model of both the granular ball support vector machine and granular ball k-nearest neighbor algorithm (GBkNN). In addition, the GBkNN is compared with the k-means tree based kNN, which is the most efficient and effective kNN as far as we known, on both public and artificial data sets. The Experimental results demonstrate the effectiveness and efficiency of the proposed framework.