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Keywords = MCIR-RGAD

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24 pages, 1317 KB  
Article
TFD-IIS-CRMCB: Telecom Fraud Detection for Incomplete Information Systems Based on Correlated Relation and Maximal Consistent Block
by Ran Li, Hongchang Chen, Shuxin Liu, Kai Wang, Biao Wang and Xinxin Hu
Entropy 2023, 25(1), 112; https://doi.org/10.3390/e25010112 - 5 Jan 2023
Cited by 14 | Viewed by 3767
Abstract
Telecom fraud detection is of great significance in online social networks. Yet the massive, redundant, incomplete, and uncertain network information makes it a challenging task to handle. Hence, this paper mainly uses the correlation of attributes by entropy function to optimize the data [...] Read more.
Telecom fraud detection is of great significance in online social networks. Yet the massive, redundant, incomplete, and uncertain network information makes it a challenging task to handle. Hence, this paper mainly uses the correlation of attributes by entropy function to optimize the data quality and then solves the problem of telecommunication fraud detection with incomplete information. First, to filter out redundancy and noise, we propose an attribute reduction algorithm based on max-correlation and max-independence rate (MCIR) to improve data quality. Then, we design a rough-gain anomaly detection algorithm (MCIR-RGAD) using the idea of maximal consistent blocks to deal with missing incomplete data. Finally, the experimental results on authentic telecommunication fraud data and UCI data show that the MCIR-RGAD algorithm provides an effective solution for reducing the computation time, improving the data quality, and processing incomplete data. Full article
(This article belongs to the Special Issue Data Science: Measuring Uncertainties II)
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