Published in: 14th International Industrial Engineering Conference
COI code: IIEC14_041
Paper Language: English
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Authors Fraud Detection With a New Composite Bagging Model Using Classiﬁer AlgorithmsAbdollah Eshghi - Phd student , Faculty of Systems and Industrial Engineering , Tarbiat Modares University
Mehrdad Kargari - Associate Professor, Faculty of Systems and Industrial Engineering , Tarbiat Modares University
Abstract:In this paper, an innovative fraud detection model built upon existing data mining and fraud detection methods has been proposed.Here a bagging model has been applied and has been compared with other methods such as Logistic Regression, Naïve Bayes and Decision tree (DD). We use these methods as basic classifiers and make a bagging model according to them. A variety of measures is used for measuring and evaluating the efficiency and performance of each classifier and then all of them with the proposedmodel. This study is based on real world dataset which has been divided into 4 smaller datasets with different fraudulent transaction rates. The proposed bagging model has shown higher performance compared to other mentioned models regarding almost all measures. The introduced model is using a virtual binary dataset which has been derived from the real life dataset.
Keywords:Bagging model,Fraud detection, Bagging, Naïve Bayes, Logistic Regression, Decision Tree;
COI code: IIEC14_041
how to cite to this paper:If you want to refer to this article in your research, you can easily use the following in the resources and references section:
Eshghi, Abdollah & Mehrdad Kargari, 2017, Fraud Detection With a New Composite Bagging Model Using Classiﬁer Algorithms, 14th International Industrial Engineering Conference, تهران, انجمن مهندسي صنايع ايران - دانشگاه علم و صنعت ايران, https://www.civilica.com/Paper-IIEC14-IIEC14_041.htmlInside the text, wherever referred to or an achievement of this article is mentioned, after mentioning the article, inside the parental, the following specifications are written.
First Time: (Eshghi, Abdollah & Mehrdad Kargari, 2017)
Second and more: (Eshghi & Kargari, 2017)
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Type: state university
Paper No.: 25939
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