Solving credit card fraud detection problem by the new metaheuristics migrating birds optimization

Name: Solving credit card fraud detection problem by the new metaheuristics migrating birds optimization
Author: Duman, Ekrem, Elikucuk, I.

İsim	Solving credit card fraud detection problem by the new metaheuristics migrating birds optimization
Yazar	Duman, Ekrem, Elikucuk, I.
Basım Tarihi:	2013
Basım Yeri	- Springer Science+Business Media
Konu	Migrating birds optimization algorithm, Fraud, Credit cards, Genetic algorithms
Tür	Belge
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	978-3-642-38682-4
Kayıt Numarası	47c9f88d-2f75-4f23-9737-154e41273454
Lokasyon	Industrial Engineering
Tarih	2013
Notlar	Due to copyright restrictions, the access to the full text of this article is only available via subscription.
Örnek Metin	Statistical fraud detection problem is a very difficult problem in that there are very few examples of fraud. The great majority of transactions are legitimate. On the other hand, for this binary classification problem the costs of the two types of classification errors (FP=false positive and FN=false negative) are not the same. Thus, the classical data mining algorithms do not fit to the problem exactly. Departing from this fact, we have solved this problem by genetic algorithms and scatter search. Now, we apply the recently developed new metaheuristics algorithm namely the migrating birds optimization algorithm (MBO) to this problem. Results show that it outperforms the former approach. The performance of standard MBO is further increased by the help of some modified benefit mechanisms.
DOI	10.1007/978-3-642-38682-4_8
Cilt	7903

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Solving credit card fraud detection problem by the new metaheuristics migrating birds optimization

Yazar Duman, Ekrem, Elikucuk, I.

Basım Tarihi 2013

Basım Yeri - Springer Science+Business Media

Konu Migrating birds optimization algorithm, Fraud, Credit cards, Genetic algorithms

Tür Belge

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 978-3-642-38682-4

Kayıt Numarası 47c9f88d-2f75-4f23-9737-154e41273454

Lokasyon Industrial Engineering

Tarih 2013

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

Örnek Metin Statistical fraud detection problem is a very difficult problem in that there are very few examples of fraud. The great majority of transactions are legitimate. On the other hand, for this binary classification problem the costs of the two types of classification errors (FP=false positive and FN=false negative) are not the same. Thus, the classical data mining algorithms do not fit to the problem exactly. Departing from this fact, we have solved this problem by genetic algorithms and scatter search. Now, we apply the recently developed new metaheuristics algorithm namely the migrating birds optimization algorithm (MBO) to this problem. Results show that it outperforms the former approach. The performance of standard MBO is further increased by the help of some modified benefit mechanisms.

DOI 10.1007/978-3-642-38682-4_8

Cilt 7903