FEATURE SELECTION TECHNIQUES

The DNA micro array technology has modernized the approach of biology research in such a way that scientists can now measure the expression levels of thousands of genes simultaneously in a single experiment. Gene expression profiles, which represent the state of a cell at a molecular level, have great potential as a medical diagnosis tool. But compared to the number of genes involved, available training data sets generally have a fairly small sample size for classification. These training data limitations constitute a challenge to certain classification methodologies. Feature selection techniques can be used to extract the marker genes which influence the classification accuracy effectively by eliminating the unwanted noisy and redundant genes This paper presents a review of feature selection techniques that have been employed in micro array data based cancer classification and also the predominant role of SVM for cancer classification.

Classification of the different categories of small peptides is a challenging research area in bioinformatics research. However, machine learning based approaches are widely experimented in the literature with enormous success. For excellent learning of the classifiers, few numbers of informative features are important. This research explores a comparative study between various supervised feature selection methods such as Document Frequency (DF), Chi-Squared ( 2  ), Information Gain (IG), Gain Ratio (GR), Relief F (RF), and One R (OR). The corpus of small peptides data is selected from ARAPEP repository. Bayesian Classifier is taken to classify the different categories of the given corpus with the help of features selected by above feature selection techniques. Results of this study shows that RF is the excellent feature selection technique amongst other in terms of classification accuracy and false positive rate whereas DF and 2  were not so effective methods. Bayesian classifier has proven its worth in this study in terms of good performance accuracy and low false positives. Small peptides Identification, Machine Learning Classifiers, Pattern Recognition