Title of article
Fraud Detection of Credit Cards Using Neuro-fuzzy Approach Based on TLBO and PSO Algorithms
Author/Authors
Ghodsi, Maryam Faculty of Computer and Information Technology Engineering - Qazvin Branch Islamic Azad University, Qazvin Department of Computer Engineering and IT - Parand Branch Islamic Azad University, Parand, Iran , Saniee Abadeh, Mohammad Faculty of Computer and Electrical Engineering - Tarbiyat Modarres University, Tehran Department of Computer Engineering and IT - Parand Branch Islamic Azad University, Parand, Iran
Pages
12
From page
57
To page
68
Abstract
The aim of this paper is to detect bank credit cards related frauds. The large amount of data and their similarity lead to a time consuming and low accurate separation of healthy and unhealthy samples behavior, by using traditional classifications. Therefore in this study, the Adaptive Neuro-Fuzzy Inference System (ANFIS) is used in order to reach a more efficient and accurate algorithm. By combining evolutionary algorithms with ANFIS, the optimal tuning of ANFIS parameters is achieved by the Teaching-Learning-Based Optimization (TLBO) and the Particle Swarm Optimization (PSO). The aim of using this approach is to improve the network performance and to reduce calculation complexities compared to gradient descent and least square methods. The proposed algorithm is implemented and evaluated on credit cards data to detect fraud. The results demonstrate superior performance of the designed scheme compared to other intelligent identification methods.
Keywords
Credit Cards Fraud Detection , Teaching-Learning-Based Optimization (TLBO) , Adaptive Neuro-Fuzzy Inference System (ANFIS) , Particle Swarm Optimization (PSO)
Serial Year
2017
Record number
2494704
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