Using Genetic Algorithm to Improve Classification of Imbalanced Datasets for Credit Card Fraud Detection
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- @InProceedings{Benchaji:2018:CSNet,
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author = "Ibtissam Benchaji and Samira Douzi and
Bouabid {El Ouahidi}",
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booktitle = "2018 2nd Cyber Security in Networking Conference
(CSNet)",
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title = "Using Genetic Algorithm to Improve Classification of
Imbalanced Datasets for Credit Card Fraud Detection",
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year = "2018",
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abstract = "With the growing usage of credit card transactions,
financial fraud crimes have also been drastically
increased leading to the loss of huge amounts in the
finance industry. Having an efficient fraud detection
method has become a necessity for all banks in order to
minimize such losses. In fact, credit card fraud
detection system involves a major challenge: the credit
card fraud data sets are highly imbalanced since the
number of fraudulent transactions is much smaller than
the legitimate ones. Thus, many of traditional
classifiers often fail to detect minority class objects
for these skewed data sets. This paper aims first: to
enhance classified performance of the minority of
credit card fraud instances in the imbalanced data set,
for that we propose a sampling method based on the
K-means clustering and the genetic algorithm. We used
K-means algorithm to cluster and group the minority
kind of sample, and in each cluster we use the genetic
algorithm to gain the new samples and construct an
accurate fraud detection classifier.",
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keywords = "genetic algorithms, genetic programming",
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DOI = "doi:10.1109/CSNET.2018.8602972",
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month = oct,
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notes = "Also known as \cite{8602972}",
- }
Genetic Programming entries for
Ibtissam Benchaji
Samira Douzi
Bouabid El Ouahidi
Citations