Dempster-Shafer Fusion of Semi-supervised Learning Methods for Predicting Defaults in Social Lending

Aleum Kim; Sung Bae Cho

doi:10.1007/978-3-319-70096-0_87

Dempster-Shafer Fusion of Semi-supervised Learning Methods for Predicting Defaults in Social Lending

Aleum Kim, Sung Bae Cho

College of Computing

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

6 Citations (Scopus)

Abstract

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.

Original language	English
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
Publisher	Springer Verlag
Pages	854-862
Number of pages	9
ISBN (Print)	9783319700953
DOIs	https://doi.org/10.1007/978-3-319-70096-0_87
Publication status	Published - 2017
Event	24th International Conference on Neural Information Processing, ICONIP 2017 - Guangzhou, China Duration: 2017 Nov 14 → 2017 Nov 18

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	10635 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Other

Other	24th International Conference on Neural Information Processing, ICONIP 2017
Country/Territory	China
City	Guangzhou
Period	17/11/14 → 17/11/18

Bibliographical note

Funding 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).

Publisher Copyright:
© 2017, Springer International Publishing AG.

All Science Journal Classification (ASJC) codes

Theoretical Computer Science
Computer Science(all)

Access to Document

10.1007/978-3-319-70096-0_87

Cite this

Kim, A., & Cho, S. B. (2017). Dempster-Shafer Fusion of Semi-supervised Learning Methods for Predicting Defaults in Social Lending. In D. Zhao, E-S. M. El-Alfy, D. Liu, S. Xie, & Y. Li (Eds.), Neural Information Processing - 24th International Conference, ICONIP 2017, Proceedings (pp. 854-862). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10635 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-70096-0_87

Kim, Aleum ; Cho, Sung Bae. / Dempster-Shafer Fusion of Semi-supervised Learning Methods for Predicting Defaults in Social Lending. Neural Information Processing - 24th International Conference, ICONIP 2017, Proceedings. editor / Dongbin Zhao ; El-Sayed M. El-Alfy ; Derong Liu ; Shengli Xie ; Yuanqing Li. Springer Verlag, 2017. pp. 854-862 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Dempster-Shafer Fusion of Semi-supervised Learning Methods for Predicting Defaults in Social Lending",

abstract = "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.",

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note = "Funding 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). Publisher Copyright: {\textcopyright} 2017, Springer International Publishing AG.; 24th International Conference on Neural Information Processing, ICONIP 2017 ; Conference date: 14-11-2017 Through 18-11-2017",

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doi = "10.1007/978-3-319-70096-0_87",

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Kim, A & Cho, SB 2017, Dempster-Shafer Fusion of Semi-supervised Learning Methods for Predicting Defaults in Social Lending. in D Zhao, E-SM El-Alfy, D Liu, S Xie & Y Li (eds), Neural Information Processing - 24th International Conference, ICONIP 2017, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10635 LNCS, Springer Verlag, pp. 854-862, 24th International Conference on Neural Information Processing, ICONIP 2017, Guangzhou, China, 17/11/14. https://doi.org/10.1007/978-3-319-70096-0_87

Dempster-Shafer Fusion of Semi-supervised Learning Methods for Predicting Defaults in Social Lending. / Kim, Aleum; Cho, Sung Bae.

Neural Information Processing - 24th International Conference, ICONIP 2017, Proceedings. ed. / Dongbin Zhao; El-Sayed M. El-Alfy; Derong Liu; Shengli Xie; Yuanqing Li. Springer Verlag, 2017. p. 854-862 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10635 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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T1 - Dempster-Shafer Fusion of Semi-supervised Learning Methods for Predicting Defaults in Social Lending

AU - Kim, Aleum

AU - Cho, Sung Bae

N1 - Funding 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). Publisher Copyright: © 2017, Springer International Publishing AG.

PY - 2017

Y1 - 2017

N2 - 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.

AB - 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.

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T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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Kim A, Cho SB. Dempster-Shafer Fusion of Semi-supervised Learning Methods for Predicting Defaults in Social Lending. In Zhao D, El-Alfy E-SM, Liu D, Xie S, Li Y, editors, Neural Information Processing - 24th International Conference, ICONIP 2017, Proceedings. Springer Verlag. 2017. p. 854-862. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-70096-0_87

Dempster-Shafer Fusion of Semi-supervised Learning Methods for Predicting Defaults in Social Lending

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