Hybridized Support Vector Machine and Recursive Feature Elimination with Information Complexity

In statistical data mining research, datasets often have nonlinearity and at the same time high-dimensionality. It has become difficult to analyze such datasets in a comprehensive manner using traditional statistical methodologies. In this paper, a novel wrapper method called SVM-ICOMP-RFE based on a hybridized support vector machine (SVM) and recursive feature elimination (RFE) with information-theoretic measure of complexity (ICOMP) is introduced and developed to classify high-dimensional data sets and to carry out subset selection of the features in the original data space for finding the best subset of features which are discriminating between the groups. Recursive feature elimination (RFE) ranks features based on information complexity (ICOMP) criterion. ICOMP plays an important role not only in choosing an optimal kernel function from a portfolio of many other kernel functions, but also in selecting important subset(s) of features. The potential and the flexibility of our approach are illustrated on two real benchmark data sets, one is ionosphere data which includes radar returns from the ionosphere, and another is aorta data which is used for the early detection of atheroma most commonly resulting heart attack. Also, the proposed method is compared with other RFE based methods using different measures (i.e., weight and gradient) for feature rankings.