Fuzzy-rough-neural-based f-information for gene selection and sample classification

The greatest restriction in estimating the information measure for microarray data is the continuous nature of gene expression values. The traditional criterion function of f-information discretises the continuous gene expression value for calculating the probability function during gene selection. This leads to loss of biological meaning of microarray data and results in poor classification accuracy. To overcome this difficulty, the concepts of fuzzy and rough set are combined to redefine the criterion functions of f-information and are used to form candidate genes from which informative genes are selected using neural network. The performance of the proposed Fuzzy-Rough-Neural-based f-Information (FRNf-I) is evaluated using ten gene expression datasets. Simulation results show that the proposed approach compute f-information measure easily without discretisation. Statistical analysis of the test result shows that the proposed FRNf-I selects comparatively less number of genes and more classification accuracy than the other approaches reported in the literature.