Aiding classification of gene expression data with feature selection: a comparative study

H...............H

Show simple item record

dc.contributor.author Shen, Qiang
dc.contributor.author Shang, Changjing
dc.date.accessioned 2008-01-24T11:37:26Z
dc.date.available 2008-01-24T11:37:26Z
dc.date.issued 2006
dc.identifier.citation Shen , Q & Shang , C 2006 , ' Aiding classification of gene expression data with feature selection: a comparative study ' Journal of Computational Intelligence Research (IJCIR) , pp. 68-76 . en
dc.identifier.issn 0973-1873
dc.identifier.other PURE: 74345
dc.identifier.other dspace: 2160/472
dc.identifier.uri http://hdl.handle.net/2160/472
dc.identifier.uri http://www.softcomputing.net/ijcir/1006.pdf en
dc.description C. Shang and Q. Shen. Aiding classification of gene expression data with feature selection: a comparative study. Computational Intelligence Research, 1(1):68-76. en
dc.description.abstract This paper presents an application of supervised machine learning approaches to the classification of the yeast S. cerevisiae gene expression data. Established feature selection techniques based on information gain ranking and principal component analysis are, for the first time, applied to this data set to support learning and classification. Different classifiers are implemented to investigate the impact of combining feature selection and classification methods. Learning classifiers implemented include K-Nearest Neighbours (KNN), Naive Bayes and Decision Trees. Results of comparative studies are provided, demonstrating that effective feature selection is essential to the development of classifiers intended for use in highdimension domains. In particular, amongst a large corpus of systematic experiments carried out, best classification performance is achieved using a subset of features chosen via information gain ranking for KNN and Naive Bayes classifiers. Naive Bayes may also perform accurately with a relatively small set of linearly transformed principal features in classifying this difficult data set. This research also shows that feature selection helps increase computational efficiency while improving classification accuracy. en
dc.format.extent 9 en
dc.language.iso eng
dc.relation.ispartof Journal of Computational Intelligence Research (IJCIR) en
dc.title Aiding classification of gene expression data with feature selection: a comparative study en
dc.type Text en
dc.type.publicationtype Article (Journal) en
dc.contributor.institution Department of Computer Science en
dc.contributor.institution Advanced Reasoning Group en
dc.description.status Peer reviewed en


Files in this item

This item appears in the following Collection(s)

Show simple item record

Search Cadair


Advanced Search

Browse

My Account