@inproceedings{c13134972e7947c88196562084d66af1,
title = "Association rule-based feature selection for credit risk assessment",
abstract = "Credit risk assessment becomes rapidly important for credit department of the bank to determine whether to issue credit cards, and make loans to both companies and individualities. However, due to the complexity of database, it is arduous for credit managers to make decisions. In order to solve this problem, this paper proposes a framework that combines feature selection and decision tree classification for credit risk assessment. The feature selection is based on association rules mining. The empirical result shows that our approach can not only reduce the data dimensionality significantly but also provide higher classification accuracies than other methods.",
keywords = "Association rule mining, Credit risk assessment, Decision trees, Feature Selection",
author = "Xueyan Mei and Yilin Jiang",
note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 2016 IEEE International Conference of Online Analysis and Computing Science, ICOACS 2016 ; Conference date: 28-05-2016 Through 29-05-2016",
year = "2016",
month = sep,
day = "17",
doi = "10.1109/ICOACS.2016.7563102",
language = "English",
series = "Proceedings of 2016 IEEE International Conference of Online Analysis and Computing Science, ICOACS 2016",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "301--305",
editor = "Guorong Chen and Jun Peng",
booktitle = "Proceedings of 2016 IEEE International Conference of Online Analysis and Computing Science, ICOACS 2016",
address = "United States",
}