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

Title	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
Publication Date:	2015
Publication Place	- World Academy of Science, Engineering and Technology
Subject	Neural network, Profit-based neural network, Sum of squared errors (SSE), MBO, Gradient descent
Type	Document
Language	English
Digital	Yes
Manuscript	No
Library:	Özyeğin University
Library Asset ID	2-s2.0-84969988953
Record ID	256064fb-5586-4bbc-915b-1ec8fce51399
Library Location	Industrial Engineering
Date	2015
Notes	Due to copyright restrictions, the access to the full text of this article is only available via subscription.
Sample Text	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

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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

Publication Date 2015

Publication Place - World Academy of Science, Engineering and Technology

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

Type Document

Language English

Digital Yes

Manuscript No

Library Özyeğin University

Library Asset ID 2-s2.0-84969988953

Record ID 256064fb-5586-4bbc-915b-1ec8fce51399

Library Location Industrial Engineering

Date 2015

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

Sample Text 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