Gene expression data classification using SVM-KNN classifier

We propose a new classifier that combines support vector machine (SVM) with K nearest neighbor (KNN) for gene expression data classification. The new classifier, SVM-KNN (KSVM), takes SVM as a 1NN classifier in which only one representative point is selected for each class. In the class phase, the algorithm computes the distance from the test samples to the optimal hyperplane of SVM in feature space. If the distance is greater that a given threshold, the test sample is classified on SVM; otherwise, the KNN algorithm is used. Experimental results show that KSVM has a higher classification rate than those of traditional SVM and KNN. A better method for the problem of gene selection is also suggested.