Gene selection in cancer classification using hybrid method based on Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC) feature selection and support vector machine

D. A. Utami, Z. Rustam

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This study proposed the Hybrid method, Particle Swarm Optimization-Support Vector Machine (PSO-SVM) and Artificial Bee Colony-Support Vector Machine (ABC-SVM), in selecting informative genes for cancer classification. PSO and ABC are filter methods for eliminating inefficient genes in high-dimensional gene expression data using ranking techniques. Top ranking genes are chosen as informative genes. While SVM is used to eliminate excessive genes after being filtered by PSO and ABC, it can produce more accurate gene expression data. The informative genes chosen by PSO-SVM and ABC-SVM will be used for cancer classification. Among the two methods, ABC-SVM is the best method in classifying cancer with an accuracy rate of 88 %. All these datasets were obtained from UCI Machine Learning Repository.

Original languageEnglish
Title of host publicationProceedings of the 4th International Symposium on Current Progress in Mathematics and Sciences, ISCPMS 2018
EditorsTerry Mart, Djoko Triyono, Ivandini T. Anggraningrum
PublisherAmerican Institute of Physics Inc.
ISBN (Electronic)9780735419155
DOIs
Publication statusPublished - 4 Nov 2019
Event4th International Symposium on Current Progress in Mathematics and Sciences 2018, ISCPMS 2018 - Depok, Indonesia
Duration: 30 Oct 201831 Oct 2018

Publication series

NameAIP Conference Proceedings
Volume2168
ISSN (Print)0094-243X
ISSN (Electronic)1551-7616

Conference

Conference4th International Symposium on Current Progress in Mathematics and Sciences 2018, ISCPMS 2018
CountryIndonesia
CityDepok
Period30/10/1831/10/18

Keywords

  • ABC-SVM
  • cancer classification
  • gene expression data
  • PSO-SVM

Fingerprint Dive into the research topics of 'Gene selection in cancer classification using hybrid method based on Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC) feature selection and support vector machine'. Together they form a unique fingerprint.

  • Cite this

    Utami, D. A., & Rustam, Z. (2019). Gene selection in cancer classification using hybrid method based on Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC) feature selection and support vector machine. In T. Mart, D. Triyono, & I. T. Anggraningrum (Eds.), Proceedings of the 4th International Symposium on Current Progress in Mathematics and Sciences, ISCPMS 2018 [020047] (AIP Conference Proceedings; Vol. 2168). American Institute of Physics Inc.. https://doi.org/10.1063/1.5132474