Credit scoring model: A combination of genetic programming and deep learning

Created by W.Langdon from gp-bibliography.bib Revision:1.7421

@InProceedings{Tran:2016:FTC,

• author = "Khiem Tran and Thanh Duong and Quyen Ho",
• booktitle = "2016 Future Technologies Conference (FTC)",
• title = "Credit scoring model: A combination of genetic programming and deep learning",
• year = "2016",
• pages = "145--149",
• 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.",
• keywords = "genetic algorithms, genetic programming",
• DOI = "doi:10.1109/FTC.2016.7821603",
• month = dec,
• notes = "Also known as \cite{7821603}",

}

Genetic Programming entries for Khiem Tran Thanh Duong Quyen Ho

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