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2004

A Novel Feature Selection Method to Improve Classification of Gene Expression Data

9 years 22 days ago
A Novel Feature Selection Method to Improve Classification of Gene Expression Data
This paper introduces a novel method for minimum number of gene (feature) selection for a classification problem based on gene expression data with an objective function to maximise the classification accuracy. The method uses a hybrid of Pearson correlation coefficient (PCC) and signal-to-noise ratio (SNR) methods combined with an evolving classification function (ECF). First, the correlation coefficients between genes in a set of thousands, is calculated. Genes, that are highly correlated across samples are considered either dependent or coregulated and form a group (a cluster). Signal-to-noise ratio (SNR) method is applied to rank the correlated genes in this group according to their discriminative power towards the classes. Genes with the highest SNR are used in a preliminary feature set as representatives of each group. An incremental algorithm that consists of selecting a minimum number of genes (variables) from the preliminary feature set, starting from one gene, is then applie...
Liang Goh, Qun Song, Nikola K. Kasabov
Added 30 Oct 2010
Updated 30 Oct 2010
Type Conference
Year 2004
Where APBC
Authors Liang Goh, Qun Song, Nikola K. Kasabov
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