Multiclass classification on brain cancer with multiple support vector machine and feature selection based on kernel function

Z. Rustam, S. A.A. Kharis

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

Abstract

Cancer is one disease that needs a proper treatment. There are more than 150 types of cancer, one of them is brain cancer. Taking advantage from microarray data, machine learning methods can be applied to help brain cancer prediction according to its type. This problem can be referred to as a multiclass classification problem. Using one versus one approach, the multiclass problem with k classes can be transformed into k(k+1)/2 binary class classification. To improve the accuracy, the features candidate will be evaluated using feature selection. In this research, Kernel Function is implemented as the feature selection method and Multiple Support Vector Machine (MSVM) method is implemented as the classification method. The results obtained showed the comparison accuracy of MSVM use and without feature selection.

Original languageEnglish
Title of host publicationProceedings of the 3rd International Symposium on Current Progress in Mathematics and Sciences 2017, ISCPMS 2017
EditorsRatna Yuniati, Terry Mart, Ivandini T. Anggraningrum, Djoko Triyono, Kiki A. Sugeng
PublisherAmerican Institute of Physics Inc.
ISBN (Electronic)9780735417410
DOIs
Publication statusPublished - 22 Oct 2018
Event3rd International Symposium on Current Progress in Mathematics and Sciences 2017, ISCPMS 2017 - Bali, Indonesia
Duration: 26 Jul 201727 Jul 2017

Publication series

NameAIP Conference Proceedings
Volume2023
ISSN (Print)0094-243X
ISSN (Electronic)1551-7616

Conference

Conference3rd International Symposium on Current Progress in Mathematics and Sciences 2017, ISCPMS 2017
Country/TerritoryIndonesia
CityBali
Period26/07/1727/07/17

Keywords

  • Brain Cancer
  • Multiclass Classification
  • Multiple Support Vector Machine

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