Name
Abstract | ||
|---|---|---|
Credit risk is characterized as the risk that borrowers will neglect to pay its advance commitments and loan obligations. It is very hard to predict the outcomes (risky borrower) manually as the evaluation of large features set is quite time consuming. That's why, we need some good predictor as classifier. The traditional k-NN is one pre-established classifier used in various domains along with credit risk predictions. The newly conceptualized ARSkNN is another such classification which reduces the runtime in predicting the outcomes and improves overall accuracy percentage of the predicted classes over Traditional k-NN. The method adopt the similarity measure which is based on the Mass estimation rather than distance estimation for predicting the K- nearest neighbor. The results were compared using WEKA 3.7.10 as tool and found significant improvement vis-a-vis the evaluation parameters by the ARSkNN method. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1007/978-3-319-74690-6_63 | INTERNATIONAL CONFERENCE ON ADVANCED MACHINE LEARNING TECHNOLOGIES AND APPLICATIONS (AMLTA2018) |
Keywords | DocType | Volume |
Classification,Nearest neighbors,ARSkNN,Credit risk | Conference | 723 |
ISSN | Citations | PageRank |
2194-5357 | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ashish Kumar | 1 | 3 | 3.45 |
| Roheet Bhatnagar | 2 | 0 | 4.39 |
| Sumit Srivastava | 3 | 0 | 0.34 |