  In most gene expression data, the number of training samples is very small
compared to the large number of genes involved in the experiments. However,
among the large amount of genes, only a small fraction is effective for
performing a certain task. Furthermore, a small subset of genes is desirable in
developing gene expression based diagnostic tools for delivering reliable and
understandable results. With the gene selection results, the cost of biological
experiment and decision can be greatly reduced by analyzing only the marker
genes. An important application of gene expression data in functional genomics
is to classify samples according to their gene expression profiles. Feature
selection (FS) is a process which attempts to select more informative features.
It is one of the important steps in knowledge discovery. Conventional
supervised FS methods evaluate various feature subsets using an evaluation
function or metric to select only those features which are related to the
decision classes of the data under consideration. This paper studies a feature
selection method based on rough set theory. Further K-Means, Fuzzy C-Means
(FCM) algorithm have implemented for the reduced feature set without
considering class labels. Then the obtained results are compared with the
original class labels. Back Propagation Network (BPN) has also been used for
classification. Then the performance of K-Means, FCM, and BPN are analyzed
through the confusion matrix. It is found that the BPN is performing well
comparatively.
