Combining GRNN and SVM Using Receiver Operating Characteristics (ROC) for Improved Classification of Non Coding RNA

The RNAs are by and large coded in to protein, but there are big classes of RNA´s, which are not being coded in to protein. These RNA are called non coding RNA (ncRNA) and are important in regulatory, catalytic, or structural roles in the cell. The ncRNAs are prevalent in all living organisms. An automated method is beneficial for classification of these ncRNAs from the rest. In this paper we are proposing a classifier fusion method to improve the classification performance which is better than performance of the stand alone classifiers. We have combined two Classifiers namely SVM and GRNN under Receiver Operating Characteristic (ROC) space using the maximum likelihood combination. The input features are generated from the basic geometric and topological properties of RNA secondary structure. Then we have tested a set of sequences which is not seen (unknown) by GRNN and SVM classifiers during training process with cross-fold verification. The results obtained by the fusion method shows better than that were individually obtained by GRNN and SVM.