Feature selection and mass classification using particle swarm optimization and support vector machine

Man To Wong, Xiangjian He, Wei Chang Yeh, Zaidah Ibrahim, Zaidah Ibrahim, Yuk Ying Chung

Research output: Chapter in Book/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)

Abstract

This paper proposes an effective technique to classify regions of interests (ROIs) of digitized mammograms into mass and normal breast tissue regions by using particle swarm optimization (PSO) based feature selection and Support Vector Machine (SVM). Twenty-three texture features were derived from the gray level co-occurrence matrix (GLCM) and gray level histogram of each ROI. PSO is used to search for the gamma and C parameters of SVM with RBF kernel which will give the best classification accuracy, using all the 23 features. Using the parameters of SVM found by PSO, PSO based feature selection is used to determine the significant features. Experimental results show that the proposed PSO based feature selection technique can find the significant features that can improve the classification accuracy of SVM. The proposed classification approach using PSO and SVM has better specificity and sensitivity when compared to other mass classification techniques.

Original languageEnglish
Title of host publicationNeural Information Processing - 21st International Conference, ICONIP 2014, Proceedings
EditorsChu Kiong Loo, Keem Siah Yap, Kok Wai Wong, Andrew Teoh, Kaizhu Huang
PublisherSpringer Verlag
Pages439-446
Number of pages8
ISBN (Electronic)9783319126425
DOIs
Publication statusPublished - 2014
Externally publishedYes
Event21st International Conference on Neural Information Processing, ICONIP 2014 - Kuching, Malaysia
Duration: 3 Nov 20146 Nov 2014

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8836
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference21st International Conference on Neural Information Processing, ICONIP 2014
Country/TerritoryMalaysia
CityKuching
Period3/11/146/11/14

Keywords

  • Feature selection
  • Mass classification
  • Particle swarm optimization
  • Support vector machine

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science (all)

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