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Credit scoring analysis using weighted k nearest neighbor
| Content Provider | Scilit |
|---|---|
| Author | Mukid, M. A. Widiharih, T. Rusgiyono, A. Prahutama, A. |
| Copyright Year | 2018 |
| Description | Journal: Journal of Physics: Conference Series Credit scoring is a quatitative method to evaluate the credit risk of loan applications. Both statistical methods and artificial intelligence are often used by credit analysts to help them decide whether the applicants are worthy of credit. These methods aim to predict future behavior in terms of credit risk based on past experience of customers with similar characteristics. This paper reviews the weighted k nearest neighbor (WKNN) method for credit assessment by considering the use of some kernels. We use credit data from a private bank in Indonesia. The result shows that the Gaussian kernel and rectangular kernel have a better performance based on the value of percentage corrected classified whose value is 82.4% respectively. |
| Related Links | http://iopscience.iop.org/article/10.1088/1742-6596/1025/1/012114/pdf |
| ISSN | 17426588 |
| e-ISSN | 17426596 |
| DOI | 10.1088/1742-6596/1025/1/012114 |
| Journal | Journal of Physics: Conference Series |
| Issue Number | 1 |
| Volume Number | 1025 |
| Language | English |
| Publisher | IOP Publishing |
| Publisher Date | 2018-05-30 |
| Access Restriction | Open |
| Subject Keyword | Journal: Journal of Physics: Conference Series Operations Research and Management Science Credit Scoring K Nearest Weighted K Nearest Neighbor |
| Content Type | Text |
| Resource Type | Article |
| Subject | Physics and Astronomy |
