A Hybrid RBF-SVM Ensemble Approach for Data Mining Applications

One of the major developments in machine learning in the past decade is the ensemble method, which finds highly accurate classifier by combining many moderately accurate component classifiers. This paper addresses using an ensemble of classification methods for data min ing applications like intrusion detection, direct market ing, and signature verificat ion. In this research work, new hybrid classification method is proposed for heterogeneous ensemble classifiers using arcing and their performances are analyzed in terms of accuracy. A Classifier ensemble is designed using a Radial Basis Function (RBF) and Support Vector Machine (SVM) as base classifiers. Here, modified train ing sets are formed by resampling from original training set; classifiers constructed using these training sets and then combined by voting. The proposed RBF-SVM hybrid system is superior to individual approach for intrusion detection, direct market ing, and signature verification in terms of classification accuracy.

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