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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Yanwen Dong |
| Copyright Year | 2007 |
| Description | Author affiliation: Fukushima Univ., Fukushima (Yanwen Dong) |
| Abstract | This paper aims to apply a relatively new learning algorithm, support vector machines (SVM), to the credit scoring problem in a small-scale student dress wholesale company. Because most of the customers are minor small businesses that do not disclose financial information, it is almost impossible to obtain their financial data. So we propose an approach to assess the customers' credit only based on daily transaction data such as sales, payments by customers, amount of overdue payment, etc. We provide the model of SVM for the credit scoring problem and discuss the appropriate choice of kennel functions and their parameters. We confirm the performance and effectiveness of the SVM model by applying it to the real problems of the company and comparing it with discriminant analysis. |
| Starting Page | 112 |
| Ending Page | 112 |
| File Size | 186225 |
| Page Count | 1 |
| File Format | |
| ISBN | 0769528821 |
| DOI | 10.1109/ICICIC.2007.128 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2007-09-05 |
| Publisher Place | Japan |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Support vector machines Clustering algorithms Companies Machine learning Predictive models Cities and towns Educational institutions Marketing and sales Performance analysis Business |
| Content Type | Text |
| Resource Type | Article |
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