Weighted linear loss multiple birth support vector machine based on information granulation for multi-class classification. (July 2017)

• Record Type: Journal Article
• Authors: Ding, Shifei; Zhang, Xiekai; An, Yuexuan; Xue, Yu
• Is Part Of: Pattern recognition. Volume 67(2017:Jul.)
• Page Start: 32
• Page End: 46
• Publication Date: 2017-07
• Subjects: Multi-class classification -- Twin support vector machine -- Multiple birth support vector machine -- Granular computing; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2017.02.011
• Languages: English
• ISSNs: 0031-3203

Abstract

Highlights: This paper presents a weighted linear loss multiple birth support vector machine based on information granulation (GWLMBSVM). GWLMBSVM divides the data into several granules and builds a set of classifiers in the mixed granules. By introducing the weighted linear loss, the proposed GWLMBSVM only needs to solve simple linear equations. The overall computational complexity of GWLMBSVM is lower than multi-class WLTSVM classifier. Abstract: Recently proposed weighted linear loss twin support vector machine (WLTSVM) is an efficient algorithm for binary classification. However, the performance of multiple WLTSVM classifier needs improvement since it uses the strategy 'one-versus-rest' with high computational complexity. This paper presents a weighted linear loss multiple birth support vector machine based on information granulation (WLMSVM) to enhance the performance of multiple WLTSVM. Inspired by granular computing, WLMSVM divides the data into several granules and builds a set of sub-classifiers in the mixed granules. By introducing the weighted linear loss, the proposed approach only needs to solve simple linear equations. Moreover, since WLMSVM uses the strategy "all-versus-one" which is the key idea of multiple birth support vector machine, the overall computational complexity of WLMSVM is lower than that of multiple WLTSVM. The effectiveness of the proposed approach is demonstrated by experimental results on artificial datasets and benchmark datasets.