A Comparison of Ensemble Classifiers used for Detection of Superimposed Fraud (original) (raw)

2019 Global Conference for Advancement in Technology (GCAT)

Abstract

In this paper we have shown a comparison of ensemble classifiers which are used for detecting mobile telecommunication fraud. k-means clustering has been used to label the data and then the ensemble techniques Boosting and Bagging techniques have been used for classification. Four relevant features are extracted from the reality-mining dataset which are used for constructing the user profile. The results shows how an ensemble technique improves the performance of the classifier and a comparative analysis has been done between the two ensemble methods by calculating their accuracy.

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