MEM: a new mixed ensemble model for identifying frauds
by Zhenhua Chen; Weili Jiang; Ma Lei; Junpeng Zhang; Jinshan Hu; Yan Xiang; Dangguo Shao
International Journal of Information and Communication Technology (IJICT), Vol. 15, No. 3, 2019

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.

Online publication date: Mon, 14-Oct-2019

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