| A Comparative Study of Data Mining Techniques for Credit Scoring in Banking | |
|---|---|
| 學年 | 102 |
| 學期 | 1 |
| 發表日期 | 2013-08-14 |
| 作品名稱 | A Comparative Study of Data Mining Techniques for Credit Scoring in Banking |
| 作品名稱（其他語言） | |
| 著者 | Huang, Shih-Chen; Day, Min-Yuh |
| 作品所屬單位 | 淡江大學資訊管理學系 |
| 出版者 | IEEE Press |
| 會議名稱 | IEEE International Conference on Information Reuse and Integration (IEEE IRI 2013) |
| 會議地點 | San Francisco, California, USA |
| 摘要 | Credit is becoming one of the most important incomes of banking. Past studies indicate that the credit risk scoring model has been better for Logistic Regression and Neural Network. The purpose of this paper is to conduct a comparative study on the accuracy of classification models and reduce the credit risk. In this paper, we use data mining of enterprise software to construct four classification models, namely, decision tree, logistic regression, neural network and support vector machine, for credit scoring in banking. We conduct a systematic comparison and analysis on the accuracy of 17 classification models for credit scoring in banking. The contribution of this paper is that we use different classification methods to construct classification models and compare classification models accuracy, and the evidence demonstrates that the support vector machine models have higher accuracy rates and therefore outperform past classification methods in the context of credit scoring in banking. |
| 關鍵字 | Classification Method;Credit Risk Score;Data Mining;SAS Enterprise Miner;Support Vector Machine (SVM) |
| 語言 | en |
| 收錄於 | EI |
| 會議性質 | 國際 |
| 校內研討會地點 | |
| 研討會時間 | 20130814~20130816 |
| 通訊作者 | myday@mail.tku.edu.tw (Min-Yuh Day) |
| 國別 | USA |
| 公開徵稿 | Y |
| 出版型式 | 電子版 |
| 出處 | Proceedings of the IEEE International Conference on Information Reuse and Integration (IEEE IRI 2013), pp.684-691 |
| 相關連結 |
機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/92543 ) |
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