Name
Abstract | ||
|---|---|---|
Credit default prediction is a topic of great importance in lending industry. Just like many real-world applications, the dataset in the task is often class-imbalanced and noisy, degrading the performance of most machine learning methods. In this paper, we propose an extension of Factorization Machines, named RobustFM, to address the problem of class-imbalance and noisiness in the credit default prediction task. The proposed RobustFM employs a smoothed asymmetric Ramp loss function, into which truncation and hinge parameters are introduced to facilitate noise tolerance and imbalanced learning. Experimental results on several real credit datasets show that RobustFM significantly outperforms state-of-the-art methods in terms of F-measure. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1007/978-3-319-97304-3_72 | PRICAI 2018: TRENDS IN ARTIFICIAL INTELLIGENCE, PT I |
Keywords | Field | DocType |
Factorization machines, Ramp loss, Imbalanced classification, Credit default prediction | Credit default swap,Truncation,Computer science,Artificial intelligence,Factorization,Noise tolerance,Hinge,Machine learning | Conference |
Volume | ISSN | Citations |
11012 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 18 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Weijian Ni | 1 | 14 | 8.09 |
| Tong Liu | 2 | 3 | 3.14 |
| Qingtian Zeng | 3 | 242 | 43.67 |
| Xianke Zhang | 4 | 0 | 0.34 |
| Hua Duan | 5 | 110 | 19.58 |
| Nengfu Xie | 6 | 18 | 8.62 |