Feature selection and classification of breast cancer diagnosis based on support vector machines

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13 Citations (Scopus)

Abstract

Support Vector Machines (SVM) is a new algorithm of data mining technique, recently received increasing popularity in machine learning community. This paper emphasizes how 1-norm SVM can be used in feature selection and smooth SVM (SSVM) for classification. As a case study, a breast cancer diagnosis was implemented. First, feature selection for support vector machines was utilized to determine the important features. Then, SSVM was used to classify the state of disease (benign or malignant) of breast cancer. As a result, SVM can achieve the state of the art performance on feature selection and classification.

Original languageEnglish
Title of host publicationProceedings - International Symposium on Information Technology 2008, ITSim
DOIs
Publication statusPublished - 2008
Externally publishedYes
EventInternational Symposium on Information Technology 2008, ITSim - Kuala Lumpur, Malaysia
Duration: 26 Aug 200829 Aug 2008

Publication series

NameProceedings - International Symposium on Information Technology 2008, ITSim
Volume1

Conference

ConferenceInternational Symposium on Information Technology 2008, ITSim
Country/TerritoryMalaysia
CityKuala Lumpur
Period26/08/0829/08/08

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