Support vector machines based on weighted scatter degree

A. Long Jin*, Xin Zhou, Chi Zhou Ye

*Corresponding author for this work

Research output: Chapter in Book or Report/Conference proceedingConference Proceedingpeer-review

1 Citation (Scopus)

Abstract

Support Vector Machines (SVMs) are efficient tools, which have been widely studied and used in many fields. However, original SVM (C-SVM) only focuses on the scatter between classes, but neglects the global information about the data which are also vital for an optimal classifier. Therefore, C-SVM loses some robustness. To solve this problem, one approach is to translate (i.e., to move without rotation or change of shape) the hyperplane according to the global characteristics of the data. However, parts of existing work using this approach are based on specific distribution assumption (S-SVM), while the rest fail to utilize the global information (GS-SVM). In this paper, we propose a simple but efficient method based on weighted scatter degree (WSD-SVM) to embed the global information into GS-SVM without any distribution assumptions. A comparison of WSD-SVM, C-SVM and GS-SVM is conducted, and the results on several data sets show the advantages of WSD-SVM.

Original languageEnglish
Title of host publicationArtificial Intelligence and Computational Intelligence - Third International Conference, AICI 2011, Proceedings
Pages620-629
Number of pages10
EditionPART 3
DOIs
Publication statusPublished - 2011
Externally publishedYes
Event3rd International Conference on Artificial Intelligence and Computational Intelligence, AICI 2011 - Taiyuan, China
Duration: 24 Sept 201125 Sept 2011

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 3
Volume7004 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference3rd International Conference on Artificial Intelligence and Computational Intelligence, AICI 2011
Country/TerritoryChina
CityTaiyuan
Period24/09/1125/09/11

Keywords

  • Hyperplane
  • Large Margin Machines
  • Scatter Degree
  • Support Vector Machines
  • Translation

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