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Paper
title

Fraud Detection With a New Composite Bagging Model Using Classifier Algorithms

Credit to Download: 1 | Page Numbers 11 | Abstract Views: 110
Year: 2017
COI code: IIEC14_041
Paper Language: English

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Authors Fraud Detection With a New Composite Bagging Model Using Classifier Algorithms

  Abdollah 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;

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COI code: IIEC14_041

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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)
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Type: state university
Paper No.: 25939
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