Isolation-based hyperbox granular classification computing

Abstract

Bottom-up and top-down are two main computing models in granular computing by which the granule set including granules with different granularities. The top-down hyperbox granular computing classification algorithm based on isolation, or IHBGrC for short, is proposed in the framework of top-down computing model. Algorithm IHBGrC defines a novel function to measure the distance between two hyperbox hgranules, which is used to judge the inclusion relation between two hyperbox granules, the meet operation is used to isolate the i th class data from the other class data, and the hyperbox granule is partitioned into some hyperbox granules which include the i th class data. We compare the performance of IHBGrC with support vector machines and HBGrC, for a number of two-class problems and multiclass problems. Our computational experiments showed that IHBGrC can both speed up training and achieve comparable generalization performance.