Publication Type : Conference Proceedings
Publisher : IEEE
Source : 2019 Global Conference for Advancement in Technology (GCAT)
Url : https://doi.org/10.1109/gcat47503.2019.8978406
Campus : Amaravati
School : School of Computing
Year : 2019
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.
Cite this Research Publication : K. Ashwini, Suvasini Panigrahi, A Comparison of Ensemble Classifiers used for Detection of Superimposed Fraud, 2019 Global Conference for Advancement in Technology (GCAT), IEEE, 2019, https://doi.org/10.1109/gcat47503.2019.8978406