Authors: Yongchang Gao; Haowen Guan; Bin Gong
Addresses: Department of Computer Science and Technology, Shandong Department of Human Resources and Social Security, Shandong University, Jinan, China ' Department of Computer Science and Technology, Shandong University, Jinan, China ' Department of Computer Science and Technology, Shandong University, Jinan, China
Abstract: Data is the high dimensional in medical insurance claims management, and there are both dense and sparse regions in these datasets, so traditional outlier detection methods are not suitable for these data. In this paper, we propose a novel method to detect the outliers for abnormal medical insurance claims. Our method consists of three core steps - feature bagging to reduce the dimensions of data, calculating the core of the object's k-nearest neighbours, and computing the outlier score for each object by measuring the amount of movement of core by sequentially increasing k. Experimental results demonstrate our method is promising to tackle this problem.
Keywords: high dimensional; feature bagging; k-nearest neighbours; data mining; outlier detection; medical insurance claims fraud.
International Journal of Computational Science and Engineering, 2019 Vol.20 No.3, pp.404 - 411
Received: 11 Nov 2016
Accepted: 17 Jul 2017
Published online: 28 Nov 2019 *