In social lending, it is hard to know whether borrowers will repay well or not. Most researchers use supervised learning for default prediction, but labeling data by hand is time-consuming. Moreover, labeling results of semi-supervised learning methods are not the same each other. In this paper, we propose a fusion method of label propagation and transductive SVM based on Dempster-Shafer theory for precisely labeling unlabeled data to improve the performance. We remove few unlabeled data with lower reliabilities in labeling results and fusion of the two results based on Dempster-Shafer theory. We have conducted experiments with supervised learning method trained with labeled unlabeled data. As a result, the proposed method produced the best accuracies, 6.15% higher than the result trained with labeled data only, and 1.3% higher than the conventional methods.
|Title of host publication||Neural Information Processing - 24th International Conference, ICONIP 2017, Proceedings|
|Editors||Dongbin Zhao, El-Sayed M. El-Alfy, Derong Liu, Shengli Xie, Yuanqing Li|
|Number of pages||9|
|Publication status||Published - 2017|
|Event||24th International Conference on Neural Information Processing, ICONIP 2017 - Guangzhou, China|
Duration: 2017 Nov 14 → 2017 Nov 18
|Name||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Other||24th International Conference on Neural Information Processing, ICONIP 2017|
|Period||17/11/14 → 17/11/18|
Bibliographical noteFunding Information:
Acknowledgements. This research was supported by the MSIT (Ministry of Science and ICT), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2017-2015-0-00369) supervised by the IITP (Institute for Information & communications Technology Promotion).
All Science Journal Classification (ASJC) codes
- Theoretical Computer Science
- Computer Science(all)