Title:
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NOVEL LEARNING STRATEGY BASED ON GENETIC
PROGRAMMING FOR CREDIT CARD FRAUD
DETECTION IN BIG DATA |
Author(s):
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Ibtissam Benchaji, Samira Douzi and Bouabid El Ouahidi |
ISBN:
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978-989-8533-92-0 |
Editors:
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Ajith P. Abraham and Jörg Roth |
Year:
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2019 |
Edition:
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Single |
Keywords:
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Big Data, Fraud Detection, Imbalanced Dataset, K-means Clustering, Genetic Programming |
Type:
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Full Paper |
First Page:
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3 |
Last Page:
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10 |
Language:
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English |
Cover:
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Full Contents:
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click to dowload
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Paper Abstract:
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Due to the growing volume of monetary transactions on the internet, credit card fraud poses many challenging issues for
banks and financial institutions, thus forcing them to continuously improve their fraud detection systems. However, current
fraud detection techniques are far from accurate and fail to minimize the false alarm rates, which can cause inconvenience
and dissatisfaction for customers in e-banking system. Furthermore, there are several factors that decrease the performance
of Fraud Detection Systems (FDS), such as skewed distribution, concept drift, supports real time detection, large amount
of data etc. In this paper, we address the highly imbalanced class issue faced by a FDS when working with big data and
propose a novel method of generation of data set's minority class based on K-means clustering method and genetic
algorithm to improve classification performance in credit card fraud detection. In our experiments, we apply the proposed
approach to synthetic unbalanced credit card fraud data set to demonstrate its effectiveness by means of the most appropriate
performance measures for fraud detection purposes. |
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