A hybrid approach to estimate true density function for gene expression data

Abstract

Accurate classification of diseases from microarray gene expression profile is a challenging task because of its high dimensional low sample data. Most of the gene selection methods discretize the continuous-valued gene expression data for estimating the marginal and joint probabilities that results in inherent error during discretization and reduces the classification accuracy. To overcome this difficulty, a hybrid fuzzy-rough set approach is proposed that generates a fuzzy equivalence class and constructs a fuzzy equivalence partition matrix to estimate the true density function for the continuous-valued gene expression data without discretization. The performance of the proposed approach is evaluated using six gene expression data. f-Information measure is used for gene selection and back propagation network is used for classification. Simulation results show that the proposed method estimate the true density function correctly without discretizing the continuous gene expression values. Further the proposed approach performs the integration required to computef-Information measure easily and results in highly informative genes that produces good classification accuracy. © 2011 Springer-Verlag.