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.
KeywordsGene Expression profiles–Fuzzy-Rough Set–
f-Information–Back Propagation Network
- SourceAvailable from: psu.eduProceedings of 3rd Asia-Pacific Bioinformatics Conference, 17-21 January 2005, Singapore; 01/2005