An Efficient Statistical Model Based Classification Algorithm for Classifying Cancer Gene Expression Data with Minimal Gene Subsets

Data mining algorithms are extensively used to classify gene expression data, in which prediction of disease plays a vital role. This paper aims to develop a new classification algorithm for cancer gene expression data using minimal number of gene combinations i.e. minimum gene subsets. The model us...

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Bibliographic Details
Published inInternational Journal of Cyber Society and Education Vol. 2; no. 2; pp. 051 - 066
Main Authors Mallika Rangasamy, Saravanan Venketraman
Format Journal Article
LanguageChinese
Published 台灣 Academy of Taiwan Information Systems Research 01.12.2009
Subjects
ANOVA P-values
Classification
Microarray Data
Prediction
SVM-OAA, LDA
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Summary:Data mining algorithms are extensively used to classify gene expression data, in which prediction of disease plays a vital role. This paper aims to develop a new classification algorithm for cancer gene expression data using minimal number of gene combinations i.e. minimum gene subsets. The model uses classical statistical technique for gene ranking and two different classifiers for gene selection and prediction. The proposed method proves the capability of producing very high accuracy with very minimum number of genes. The methodology was tried with three publicly available cancer databases and the results were compared with the earlier approaches and proven better and promising prediction strength with less computational burden. This paper also focuses on the importance of applying an efficient gene selection method prior to classification can lead to good performance and the results are proven to be the best.
ISSN:1995-6649