Hybrid Approach to Improving the Results of the SVM Classification Using the Random Forest Algorithm

Abstract

The aim of this work is to improve the results of the SVM classification (Support Vector Machine) by hybridizing the SVM classifier with the random forest classifier (Random Forest, RF) used as the auxiliary. Specification of the classification decisions obtained on the basis of the SVM classifier is performed for the objects located in the experimentally determined subareas near the hyperplane separating the classes and including both correctly and erroneously classified objects. In the case of improving the quality of the objects classification from the initial dataset, the proposed hybrid approach to the objects classification can be recommended for classification of new objects. When developing the SVM classifier, the fixed default parameters values are used. A comparative analysis of the classification results obtained during the computational experiments in the hybridization of the SVM classifier with two auxiliary classifiers – the random forest classifier (RF classifier) and the k nearest neighbor classifier (kNN classifier), for which the parameters values are determined randomly, confirms the expediency of using of these classifiers to increase the SVM classification quality. It was found that in most cases, the random forest classifier works better in terms of improving the SVM classification quality in comparison with the kNN classifier.