Feature Extraction Method Based on Social Network Analysis

• Published in: Applied artificial intelligence Vol. 33; no. 8; pp. 669 - 688
• Main Authors: Zandian, Zahra Karimi, Keyvanpour, Mohammad Reza
• Format: Journal Article
• Language: English
• Published: Philadelphia Taylor & Francis 03.07.2019; Taylor & Francis Ltd; Taylor & Francis Group
• Subjects: Electronic commerce; Feature extraction; Fraud; Network analysis; Run time (computers); Social network analysis; Social networks
• ISSN: 0883-9514; 1087-6545
• DOI: 10.1080/08839514.2019.1592347

Due to rapid development of Internet technology and electronic business, fraudulent activities have increased. One of the ways to cope with damages of them is fraud detection. In this field, there is a need for methods accurate and fast. Therefore, a novel and efficient feature extraction method based on social network analysis called FEMBSNA is proposed for fraud detection in banking accounts. In this method, in order to increase accuracy and control runtime in the first step, features based on network level are considered using social network analysis and extracted feature is combined with other features based on user level in the next phase. To evaluate our feature extraction method, we use PCK-means method as a basic method to learn. The results show using the proposed feature extraction as a pre-processing step in fraud detection improves the accuracy remarkably while it controls runtime in comparison with other methods.