Title: MEM: a new mixed ensemble model for identifying frauds

Authors: Zhenhua Chen; Weili Jiang; Ma Lei; Junpeng Zhang; Jinshan Hu; Yan Xiang; Dangguo Shao

Addresses: Department of Information Engineering and Automation, Kunming University of Science and Technology, No. 727, Jingming South Road, Kunming, Yunnan Province, China ' Department of Information Engineering and Automation, Kunming University of Science and Technology, No. 727, Jingming South Road, Kunming, Yunnan Province, China ' Department of Information Engineering and Automation, Kunming University of Science and Technology, No. 727, Jingming South Road, Kunming, Yunnan Province, China ' Department of Engineering, Dali University, No. 2, Hongsheng Road, Dali, Yunnan Province, China ' Department of Information Engineering and Automation, Kunming University of Science and Technology, No. 727, Jingming South Road, Kunming, Yunnan Province, China ' Department of Information Engineering and Automation, Kunming University of Science and Technology, No. 727, Jingming South Road, Kunming, Yunnan Province, China ' Department of Information Engineering and Automation, Kunming University of Science and Technology, No. 727, Jingming South Road, Kunming, Yunnan Province, China

Abstract: In the social security system, there still exist wilful insurance frauds. In this paper, to address the insufficient stability and randomness of the traditional insurance fraud evaluation model, we propose a new classifier called mixed ensemble model (MEM). Based on the principle of ensemble learning, MEM combines several different individual learners and uses Q statistical methods to evaluate diversity. MEM has been tested on two fraud related datasets to compare with three state-of-the-art classifiers: neural network, naive Bayes and logistic regression. The experimental results show that MEM performs better than the other three classifiers in both datasets under the four measures: accuracy, recall, F-value and kappa. MEM can be a useful method for the detection of social insurance fraud.

Keywords: detection of social insurance frauds; measurement of social insurance frauds; social insurance identification techniques; mixed ensemble model; MEM.

DOI: 10.1504/IJICT.2019.103002

International Journal of Information and Communication Technology, 2019 Vol.15 No.3, pp.294 - 303

Available online: 14 Oct 2019 *

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