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Detecting credit card fraud by modified Fisher discriminant analysis

عنوان	Detecting credit card fraud by modified Fisher discriminant analysis
نویسنده	Mahmoudi, Nader, Duman, Ekrem
تاریخ انتشار:	20115-04-01
محل انتشار	- Elsevier
موضوع	Credit card fraud, Linear discriminant, Fisher linear discriminant function, Modified Fisher discriminant, Profitability
نوع	دوره ای
زبان	انگلیسی
دیجیتال	بله
نسخه خطی	خیر
کتابخانه:	دانشگاه اوزیغین
شناسه دارایی کتابخانه	1873-6793
شماره ثبت	66562783-4101-4ace-a1f4-b7e32baa0d9b
محل کتابخانه	Industrial Engineering
تاریخ	20115-04-01
یادداشت‌ها	Due to copyright restrictions, the access to the full text of this article is only available via subscription.
متن نمونه	In parallel to the increase in the number of credit card transactions, the financial losses due to fraud have also increased. Thus, the popularity of credit card fraud detection has been increased both for academicians and banks. Many supervised learning methods were introduced in credit card fraud literature some of which bears quite complex algorithms. As compared to complex algorithms which somehow over-fit the dataset they are built on, one can expect simpler algorithms may show a more robust performance on a range of datasets. Although, linear discriminant functions are less complex classifiers and can work on high-dimensional problems like credit card fraud detection, they did not receive considerable attention so far. This study investigates a linear discriminant, called Fisher Discriminant Function for the first time in credit card fraud detection problem. On the other hand, in this and some other domains, cost of false negatives is very higher than false positives and is different for each transaction. Thus, it is necessary to develop classification methods which are biased toward the most important instances. To cope for this, a Modified Fisher Discriminant Function is proposed in this study which makes the traditional function more sensitive to the important instances. This way, the profit that can be obtained from a fraud/legitimate classifier is maximized. Experimental results confirm that Modified Fisher Discriminant could eventuate more profit.
DOI	10.1016/j.eswa.2014.10.037
Cilt	42

مشاهده در منبع دانشگاه اوزیغین دانشگاه اوزیغین - موتور جستجوی نسخه های خطی عثمانی

دانشگاه اوزیغین

Detecting credit card fraud by modified Fisher discriminant analysis

نویسنده Mahmoudi, Nader, Duman, Ekrem

تاریخ انتشار 20115-04-01

محل انتشار - Elsevier

موضوع Credit card fraud, Linear discriminant, Fisher linear discriminant function, Modified Fisher discriminant, Profitability

نوع دوره ای

زبان انگلیسی

دیجیتال بله

نسخه خطی خیر

کتابخانه دانشگاه اوزیغین

شناسه دارایی کتابخانه 1873-6793

شماره ثبت 66562783-4101-4ace-a1f4-b7e32baa0d9b

محل کتابخانه Industrial Engineering

تاریخ 20115-04-01

یادداشت‌ها Due to copyright restrictions, the access to the full text of this article is only available via subscription.

متن نمونه In parallel to the increase in the number of credit card transactions, the financial losses due to fraud have also increased. Thus, the popularity of credit card fraud detection has been increased both for academicians and banks. Many supervised learning methods were introduced in credit card fraud literature some of which bears quite complex algorithms. As compared to complex algorithms which somehow over-fit the dataset they are built on, one can expect simpler algorithms may show a more robust performance on a range of datasets. Although, linear discriminant functions are less complex classifiers and can work on high-dimensional problems like credit card fraud detection, they did not receive considerable attention so far. This study investigates a linear discriminant, called Fisher Discriminant Function for the first time in credit card fraud detection problem. On the other hand, in this and some other domains, cost of false negatives is very higher than false positives and is different for each transaction. Thus, it is necessary to develop classification methods which are biased toward the most important instances. To cope for this, a Modified Fisher Discriminant Function is proposed in this study which makes the traditional function more sensitive to the important instances. This way, the profit that can be obtained from a fraud/legitimate classifier is maximized. Experimental results confirm that Modified Fisher Discriminant could eventuate more profit.

DOI 10.1016/j.eswa.2014.10.037

Cilt 42