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An Ensemble Correlation-Based Gene Selection Algorithm for Cancer Classification with Gene Expression Data.
MedLine Citation:
PMID:  23060613     Owner:  NLM     Status:  Publisher    
Abstract/OtherAbstract:
MOTIVATION: Gene selection for cancer classification is one of the most important topics in the biomedical field. However, microarray data poses a severe challenge for computational techniques. We need dimension reduction techniques that identify a small set of genes to achieve better learning performance. From the perspective of machine learning, the selection of genes can be considered to be a feature selection problem that aims to find a small subset of features that has the most discriminative information for the target. RESULTS: In this paper, we proposed an Ensemble Correlation-Based Gene Selection (ECBGS) algorithm based on symmetrical uncertainty (SU) and Support Vector Machine (SVM). In our method, symmetrical uncertainty was used to analyze the relevance of the genes, the different starting points of the relevant subset were used to generate the gene subsets, and the SVM was used as an evaluation criterion of the wrapper. The efficiency and effectiveness of our method were demonstrated through comparisons with other feature selection techniques, and the results show that our method outperformed other methods published in the literature.Availability and implementation: A java implementation of the proposed method and the datasets used for the experiments are available at ftp://210.125.145.162/ECBGS.zip CONTACT: pyz@dblab.chungbuk.ac.kr; khryu@dblab.cbnu.ac.kr.
Authors:
Yongjun Piao; Minghao Piao; Kiejung Park; Keun Ho Ryu
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Publication Detail:
Type:  JOURNAL ARTICLE     Date:  2012-10-11
Journal Detail:
Title:  Bioinformatics (Oxford, England)     Volume:  -     ISSN:  1367-4811     ISO Abbreviation:  Bioinformatics     Publication Date:  2012 Oct 
Date Detail:
Created Date:  2012-10-12     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  9808944     Medline TA:  Bioinformatics     Country:  -    
Other Details:
Languages:  ENG     Pagination:  -     Citation Subset:  -    
Affiliation:
Department of Electrical and Computer Engineering, Chungbuk National University, Chungbuk, Korea.
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