TFD-IIS-CRMCB: Telecom Fraud Detection for Incomplete Information Systems Based on Correlated Relation and Maximal Consistent Block.
Entropy (Basel)
; 25(1)2023 Jan 05.
Article
en En
| MEDLINE
| ID: mdl-36673253
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 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.
Texto completo:
1
Base de datos:
MEDLINE
Tipo de estudio:
Diagnostic_studies
Idioma:
En
Revista:
Entropy (Basel)
Año:
2023
Tipo del documento:
Article
País de afiliación:
China