Name
Abstract | ||
|---|---|---|
Imbalanced credit data sets refer to databases in which the class of defaulters is heavily under-represented in comparison to the class of non-defaulters. This is a very common situation in real-life credit scoring applications, but it has still received little attention. This paper investigates whether data resampling can be used to improve the performance of learners built from imbalanced credit data sets, and whether the effectiveness of resampling is related to the type of classifier. Experimental results demonstrate that learning with the resampled sets consistently outperforms the use of the original imbalanced credit data, independently of the classifier used. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1007/978-3-642-34481-7_9 | ICONIP |
Keywords | Field | DocType |
common situation,data resampling,original imbalanced credit data,improving risk prediction,resampled set,imbalanced credit data set,real-life credit,resampling,finance,classification | Data resampling,Data mining,Data set,Computer science,Preprocessor,Artificial intelligence,Classifier (linguistics),Resampling,Machine learning | Conference |
Volume | ISSN | Citations |
7664 | 0302-9743 | 5 |
PageRank | References | Authors |
0.44 | 13 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Vicente García | 1 | 124 | 10.85 |
| A. I. Marqués | 2 | 209 | 10.40 |
| J. Salvador Sánchez | 3 | 139 | 14.01 |