TPOT on Increasing the Performance of Credit Card Application Approval Classification
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- @InProceedings{Putrada:2022:ICICyTA,
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author = "Aji Gautama Putrada and Etika Khusnul Laeli and
Syafrial Fachri Pane and Nur Alamsyah and
Mohamad Nurkamal Fauzan",
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title = "{TPOT} on Increasing the Performance of Credit Card
Application Approval Classification",
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booktitle = "2022 2nd International Conference on Intelligent
Cybernetics Technology \& Applications (ICICyTA)",
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year = "2022",
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pages = "216--221",
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abstract = "Credit approval or credit card approval with the help
of machine learning classification has become a hot
topic in recent years. However, credit card approval
classifications from previous papers have limited
performance. Therefore, we propose evaluating
tree-based pipeline optimization (TPOT) as the
classification model creation automation for credit
card approval in this study. The data used is 690,
namely the credit card approval dataset sourced from
Kaggle, which we obtained from the University of
California learning machine and is publicly available.
Then we compare the TPOT result model with the credit
card approval classification from the related paper as
a benchmark, namely naive Bayes, support vector machine
(SVM), and decision tree. The test results show that
the TPOT model of credit card approval classification
has higher accuracy than the benchmark model of the
related research. The results of the TPOT have accuracy
= 0.89. While the accuracy of naive Bayes, SVM, and
decision tree are 0.823, 0.852, and 0.796,
respectively. Then we also see that the TPOT model
results can produce a receiver operating curve (ROC)
with the highest area under curve (AUC) value compared
to the benchmark method, which is 0.940. AUC naive
Bayes method, SVM, and decision tree are 0.897, 0.918,
and 0.796, respectively.",
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keywords = "genetic algorithms, genetic programming, Support
vector machines, Pipelines, Receivers, Benchmark
testing, Credit cards, Naive Bayes methods, credit card
approval, tree-based pipeline optimization",
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DOI = "doi:10.1109/ICICyTA57421.2022.10038063",
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month = dec,
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notes = "Also known as \cite{10038063}",
- }
Genetic Programming entries for
Aji Gautama Putrada
Etika Khusnul Laeli
Syafrial Fachri Pane
Nur Alamsyah
Mohamad Nurkamal Fauzan
Citations