Weighted linear loss multiple birth support vector machine based on information granulation for multi-class classification

作者：

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.

摘要

•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.

论文关键词：Multi-class classification,Twin support vector machine,Multiple birth support vector machine,Granular computing

论文评审过程：Received 23 November 2015, Revised 5 September 2016, Accepted 3 February 2017, Available online 4 February 2017, Version of Record 10 February 2017.

论文官网地址：https://doi.org/10.1016/j.patcog.2017.02.011