Comparison of Support Vector Machine Recursive Feature Elimination and Kernel Function as feature selection using Support Vector Machine for lung cancer classification

Z. Rustam, S. A.A. Kharis

Research output: Contribution to journalConference article

Abstract

Cancer is the uncontrolled growth of abnormal cell that need a proper treatment. Cancer is second leading cause of death according to the World Health Organization in 2018. There are more than 120 types of cancer, one of them is lung cancer. Cancer classification has been able to maximize diagnosis, treatment, and management of cancer. Many studies have examined the classification of cancer using microarrays data. Microarray data consists of thousands of features (genes) but only has dozens or hundreds of samples. This can reduce the accuracy of classification so that the selection of features is needed before the classification process. In this research, the feature selection methods are Support Vector Machine Recursive Feature Elimination (SVM-RFE) and Kernel Function and the classification method is Support Vector Machine (SVM). The results showed SVM using SVM-RFE as feature selection is better than SVM method without using feature selection and Gaussian Kernel Function.

Original languageEnglish
Article number012027
JournalJournal of Physics: Conference Series
Volume1442
Issue number1
DOIs
Publication statusPublished - 29 Jan 2020
EventBasic and Applied Sciences Interdisciplinary Conference 2017, BASIC 2017 - , Indonesia
Duration: 18 Aug 201719 Aug 2017

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Keywords

  • Kernel function
  • lung cancer
  • recursive feature elimination
  • support vector machine

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