Random forest for gene selection and microarray data classification

Kohbalan Moorthy; Mohd Saberi Mohamad

doi:10.1007/978-3-642-32826-8_18

Random forest for gene selection and microarray data classification

Kohbalan Moorthy
, Mohd Saberi Mohamad

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

15 Citations (Scopus)

Abstract

A random forest method has been selected to perform both gene selection and classification of the microarray data. The goal of this research is to develop and improve the random forest gene selection method. Hence, improved gene selection method using random forest has been proposed to obtain the smallest subset of genes as well as biggest subset of genes prior to classification. In this research, ten datasets that consists of different classes are used, which are Adenocarcinoma, Brain, Breast (Class 2 and 3), Colon, Leukemia, Lymphoma, NCI60, Prostate and Small Round Blue-Cell Tumor (SRBCT). Enhanced random forest gene selection has performed better in terms of selecting the smallest subset as well as biggest subset of informative genes through gene selection. Furthermore, the classification performed on the selected subset of genes using random forest has lead to lower prediction error rates compared to existing method and other similar available methods.

Original language	English
Title of host publication	Knowledge Technology - Third Knowledge Technology Week, KTW 2011, Revised Selected Papers
Pages	174-183
Number of pages	10
DOIs	https://doi.org/10.1007/978-3-642-32826-8_18
Publication status	Published - 2012
Externally published	Yes
Event	3rd Knowledge Technology Week, KTW 2011 - Kajang, Malaysia Duration: Jul 18 2011 → Jul 22 2011

Publication series

Name	Communications in Computer and Information Science
Volume	295 CCIS
ISSN (Print)	1865-0929

Conference

Conference	3rd Knowledge Technology Week, KTW 2011
Country/Territory	Malaysia
City	Kajang
Period	7/18/11 → 7/22/11

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

cancer classification
classification
gene expression data
gene selection
microarray data
Random forest

ASJC Scopus subject areas

General Computer Science
General Mathematics

Access to Document

10.1007/978-3-642-32826-8_18

Cite this

Random forest for gene selection and microarray data classification. / Moorthy, Kohbalan; Mohamad, Mohd Saberi.
Knowledge Technology - Third Knowledge Technology Week, KTW 2011, Revised Selected Papers. 2012. p. 174-183 (Communications in Computer and Information Science; Vol. 295 CCIS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Moorthy, K & Mohamad, MS 2012, Random forest for gene selection and microarray data classification. in Knowledge Technology - Third Knowledge Technology Week, KTW 2011, Revised Selected Papers. Communications in Computer and Information Science, vol. 295 CCIS, pp. 174-183, 3rd Knowledge Technology Week, KTW 2011, Kajang, Malaysia, 7/18/11. https://doi.org/10.1007/978-3-642-32826-8_18

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Random forest for gene selection and microarray data classification

Abstract

Publication series

Conference

UN SDGs

Keywords

ASJC Scopus subject areas

Access to Document

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