An improved multiple birth support vector machine for pattern classification

Xiekai Zhang

doi:10.1016/j.neucom.2016.11.006

Return

An improved multiple birth support vector machine for pattern classification

2017-02-01

PRE

DOI：10.1016/j.neucom.2016.11.006

Original

Original request for help

Save

Abstract

En 中文

Multiple birth supportvector machine is a novel machine learning algorithm for multi-class classification, which is considered as an extension of twin support vector machine. Compared with training speeds of other multi class classifiers based on twin support vector machine, the training speed of multiple birth support vector machine is faster, especially when the number of class is large. However, one of the disadvantages of multiple birth support vector machine is that when used to deal with some datasets such as Cross planes datasets, multiple birth support vector machine is likely to get bad results. In order to deal with this, we propose an improved multiple birth support vector machine. We add a modified item into multiple birth support vector machine to make the variance of the distances from each samples of a given class to their hyperplanes as small as possible. To predict a new sample, our method first determines an interval for each class depending on the distances between training samples and their hyperplanes, and then classifies the new sample depending on the distances between hyperplanes and the new sample which are in the corresponding intervals. In addition, smoothing technique is applied on our model, the first time it was used in multi-class twin support vector machine. The experimental results on artificial datasets and UCI datasets show that the proposed algorithm is efficient and has good classification performance.

Keywords:

Support vector machine

Twin support vector machine

Multiple birth support vector machine

Multi-class classification

Smoothing technique

AI Summary

Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.