Feature Extraction Method Based on Social Network Analysis. Issue 8 (3rd July 2019)

• Record Type: Journal Article
• Authors: Zandian, Zahra Karimi; Keyvanpour, Mohammad Reza
• Is Part Of: Applied artificial intelligence. Volume 33:Issue 8(2019)
• Page Start: 669
• Page End: 688
• Publication Date: 2019-07-03
• Subjects: Artificial intelligence -- Periodicals; 006.3
• DOI: 10.1080/08839514.2019.1592347
• Languages: English
• ISSNs: 0883-9514

Abstract

ABSTRACT: 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.