Profit-based artificial neural network (ANN) trained by migrating birds optimization: a case study in credit card fraud detection

Name: Profit-based artificial neural network (ANN) trained by migrating birds optimization: a case study in credit card fraud detection
Author: Zakaryazad, Ashkan, Duman, Ekrem, Kibekbaev, Azamat

İsim	Profit-based artificial neural network (ANN) trained by migrating birds optimization: a case study in credit card fraud detection
Yazar	Zakaryazad, Ashkan, Duman, Ekrem, Kibekbaev, Azamat
Basım Tarihi:	2015
Basım Yeri	- World Academy of Science, Engineering and Technology
Konu	Neural network, Profit-based neural network, Sum of squared errors (SSE), MBO, Gradient descent
Tür	Belge
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	2-s2.0-84969988953
Kayıt Numarası	256064fb-5586-4bbc-915b-1ec8fce51399
Lokasyon	Industrial Engineering
Tarih	2015
Notlar	Due to copyright restrictions, the access to the full text of this article is only available via subscription.
Örnek Metin	A typical classification technique ranks the instances in a data set according to the likelihood of belonging to one (positive) class. A credit card (CC) fraud detection model ranks the transactions in terms of probability of being fraud. In fact, this approach is often criticized, because firms do not care about fraud probability but about the profitability or costliness of detecting a fraudulent transaction. The key contribution in this study is to focus on the profit maximization in the model building step. The artificial neural network proposed in this study works based on profit maximization instead of minimizing the error of prediction. Moreover, some studies have shown that the back propagation algorithm, similar to other gradient–based algorithms, usually gets trapped in local optima and swarm-based algorithms are more successful in this respect. In this study, we train our profit maximization ANN using the Migrating Birds optimization (MBO) which is introduced to literature recently.
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Profit-based artificial neural network (ANN) trained by migrating birds optimization: a case study in credit card fraud detection

Yazar Zakaryazad, Ashkan, Duman, Ekrem, Kibekbaev, Azamat

Basım Tarihi 2015

Basım Yeri - World Academy of Science, Engineering and Technology

Konu Neural network, Profit-based neural network, Sum of squared errors (SSE), MBO, Gradient descent

Tür Belge

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 2-s2.0-84969988953

Kayıt Numarası 256064fb-5586-4bbc-915b-1ec8fce51399

Lokasyon Industrial Engineering

Tarih 2015

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

Örnek Metin A typical classification technique ranks the instances in a data set according to the likelihood of belonging to one (positive) class. A credit card (CC) fraud detection model ranks the transactions in terms of probability of being fraud. In fact, this approach is often criticized, because firms do not care about fraud probability but about the profitability or costliness of detecting a fraudulent transaction. The key contribution in this study is to focus on the profit maximization in the model building step. The artificial neural network proposed in this study works based on profit maximization instead of minimizing the error of prediction. Moreover, some studies have shown that the back propagation algorithm, similar to other gradient–based algorithms, usually gets trapped in local optima and swarm-based algorithms are more successful in this respect. In this study, we train our profit maximization ANN using the Migrating Birds optimization (MBO) which is introduced to literature recently.

Cilt 2