Credit scoring model: A combination of genetic programming and deep learning
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- @InProceedings{Tran:2016:FTC,
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author = "Khiem Tran and Thanh Duong and Quyen Ho",
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booktitle = "2016 Future Technologies Conference (FTC)",
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title = "Credit scoring model: A combination of genetic
programming and deep learning",
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year = "2016",
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pages = "145--149",
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abstract = "In recent years, the market of customer lending grows
rapidly, that is a reason why credit scoring becomes a
core task of financial institutes. Many models based on
machine learning have been widely using and providing
robust performance. Because most machine learning based
models are black-box, it is hard to see the relations
between input data and scoring results. Therefore, this
paper focuses on improving both the accuracy and the
reliability of machine learning based model. Thus, we
propose a hybrid idea to combine the power of deep
learning network and the comprehensive genetic
programming which is extracted rules to build a robust
credit model. Our empirical experiment on
Australian/German customer credit data sets shows that
our model provides the best accuracy, highly reduce
credit risk, and reliable IF-THEN rules.",
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keywords = "genetic algorithms, genetic programming",
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DOI = "
doi:10.1109/FTC.2016.7821603",
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month = dec,
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notes = "Also known as \cite{7821603}",
- }
Genetic Programming entries for
Khiem Tran
Thanh Duong
Quyen Ho
Citations