Paper Details

Various recent research have shown that microarray gene expression data is useful for cancerclassification and microarray based gene expression profiling has turned out to be most vital and promising dataset forthe purpose of cancer classification that are used for effective diagnosis and prognosis. It is extremely vital to determinethe most informative and defective genes in order to improve premature cancer diagnosis and to provide effectivechemotherapy processes. In addition, in order to find perfect gene selection methods that considerably reduce thedimensionality and choose informative genes is extremely noteworthy issue in the field of cancer classification. Here, inthis work, at first preprocessing process is done with the assistance of Probabilistic Principle Component Analysis(PPCA) in order to discover the Mutual Information detection on Micro array dataset and to effectively diminish thenoise included in the dataset. Then, by using the preprocessed dataset an Support Vector Machine Recursive FeatureElimination with Minimum Redundancy–Maximum Relevancy (SVM-RFE with MRMR Filter) algorithm is proposed tominimize the redundancy among the selected genes. It also improves the accuracy of classification and yields smallergene sets on several benchmark cancer gene expression datasets. This method outperforms compared to other populargene selection methods. The RF-SVM (Random Forest-SVM Classifies) algorithm 2 is applied to classify the genes andour experimental results shows that the proposed algorithm classifies accurately compare to other existing algorithms.The SVM-RFE with MRMR Filter algorithm 1 which is applied before classification for feature selection also performedwell with small amount of predictive genes when tested using both datasets and compared against previously suggestedschemes. Finally the result proves that the proposed RF-SVM (Random forest - SVM classifier) is a promising approachfor cancer classification problems

Ms. N. Kanchana, MCA, M.Phil.(Ph.D), Dr.N.Muthumani,M.Sc.(CC), M.Phil, Ph.D, “Microarray Gene Expression Data Pre-Processing Using PPCA and Classification using RF-SVM Algorithm”, International Journal of Advance Engineering and Research Development (IJAERD), Vol. 4, Issue 9, pp. 262-278, September 2017.