Performance of Feed Forward Neural Network for a Novel Feature Selection Approach

Feature selection for classification of cancer data is to discover gene expression profiles of diseased and healthy tissues and use the knowledge to predict the health state of new sample. It is usually impractical to go through all the details of the features before picking up the right features. The differentially expressed genes or biomarker gene selection is the preprocessing task for cancer classification. In this paper, we have compared the results of two approaches for selecting biomarkers from Leukaemia data set for feed forward neural networks. The first approach for feature selection is by implementing k-means clustering and signal-to-noise ratio (SNR) method for gene ranking, the top scored genes from each cluster is selected and given to the classifiers. The second approach uses signal to noise ratio ranking only for feature selection. For validation of both the approaches we have used Holdout validation and compared the results. Keywords— Differentially Expressed Genes, Feature Selection, Kmeans, Signal to Noise ratio, Feed forward neural network