Isolation-based hyperbox granular classification computing. Issue 2 (June 2017)

• Record Type: Journal Article
• Authors: Liu, Hongbing; Zhang, Fan; Li, Ran; Wu, Chang-an
• Is Part Of: Journal of algorithms & computational technology. Volume 11:Issue 2(2017)
• Page Start: 110
• Page End: 125
• Publication Date: 2017-06
• Subjects: Hyperbox granule -- granular computing -- inclusion measure -- isolation -- classification problems; Computer algorithms -- Periodicals; Numerical calculations -- Periodicals; Computer algorithms; Numerical calculations; Periodicals; 518.1
• DOI: 10.1177/1748301816676818
• Languages: English
• ISSNs: 1748-3018

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.