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Credit Card Fraud Detection: An Improved Strategy for High Recall Using KNN, LDA, and Linear Regression.
Chung, Jiwon; Lee, Kyungho.
Afiliación
  • Chung J; School of Cybersecurity, Korea University, Seoul 02841, Republic of Korea.
  • Lee K; School of Cybersecurity, Korea University, Seoul 02841, Republic of Korea.
Sensors (Basel) ; 23(18)2023 Sep 10.
Article en En | MEDLINE | ID: mdl-37765845
Efficiently and accurately identifying fraudulent credit card transactions has emerged as a significant global concern along with the growth of electronic commerce and the proliferation of Internet of Things (IoT) devices. In this regard, this paper proposes an improved algorithm for highly sensitive credit card fraud detection. Our approach leverages three machine learning models: K-nearest neighbor, linear discriminant analysis, and linear regression. Subsequently, we apply additional conditional statements, such as "IF" and "THEN", and operators, such as ">" and "<", to the results. The features extracted using this proposed strategy achieved a recall of 1.0000, 0.9701, 1.0000, and 0.9362 across the four tested fraud datasets. Consequently, this methodology outperforms other approaches employing single machine learning models in terms of recall.
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Texto completo: 1 Base de datos: MEDLINE Tipo de estudio: Diagnostic_studies Idioma: En Revista: Sensors (Basel) Año: 2023 Tipo del documento: Article

Texto completo: 1 Base de datos: MEDLINE Tipo de estudio: Diagnostic_studies Idioma: En Revista: Sensors (Basel) Año: 2023 Tipo del documento: Article