A Hybrid RBF-SVM Ensemble Approach for Data Mining Applications

Автор: M.Govindarajan

Журнал: International Journal of Intelligent Systems and Applications(IJISA) @ijisa

Статья в выпуске: 3 vol.6, 2014 года.

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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 mining applications like intrusion detection, direct marketing, and signature verification. 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 training 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 marketing, and signature verification in terms of classification accuracy.

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Machine learning, Radial Basis Function, Support Vector Machine, Intrusion Detection, Direct Marketing, Signature Verification, Ensemble, Classification Accuracy

Короткий адрес: https://sciup.org/15010541

IDR: 15010541

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