Empirical Analysis of Bagged SVM Classifier for Data Mining Applications

Empirical Analysis of Bagged SVM Classifier for Data Mining Applications

Автор: M.Govindarajan

Журнал: International Journal of Modern Education and Computer Science (IJMECS) @ijmecs

Статья в выпуске: 10 vol.5, 2013 года.

Бесплатный доступ

Data mining is the use of algorithms to extract the information and patterns derived by the knowledge discovery in databases process. Classification maps data into predefined groups or classes. It is often referred to as supervised learning because the classes are determined before examining the data. The feasibility and the benefits of the proposed approaches are demonstrated by the means of data mining applications like intrusion detection, direct marketing, and signature verification. A variety of techniques have been employed for analysis ranging from traditional statistical methods to data mining approaches. Bagging and boosting are two relatively new but popular methods for producing ensembles. In this work, bagging is evaluated on real and benchmark data sets of intrusion detection, direct marketing, and signature verification in conjunction with as the base learner. The proposed is superior to individual approach for data mining applications in terms of classification accuracy.

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Data Mining, Support Vector Machine, Intrusion Detection, Direct Marketing, Signature Verification, Classification Accuracy, Ensemble Method

Короткий адрес: https://sciup.org/15014596

IDR: 15014596

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