Abstract
Internet loan business has received extensive attentions recently. How to provide lenders with accurate credit scoring profiles of borrowers becomes a challenge due to the tremendous amount of loan requests and the limited information of borrowers. However, existing approaches are not suitable to Internet loan business due to the unique features of individual credit data. In this paper, we propose a unified data mining framework consisting of feature transformation, feature selection and hybrid model to solve the above challenges. Extensive experiment results on realistic datasets show that our proposed framework is an effective solution.
Original language | English |
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Title of host publication | Collaborate Computing: Networking, Applications and Worksharing |
Subtitle of host publication | 12th International Conference, CollaborateCom 2016, Beijing, China, November 10–11, 2016, Proceedings |
Editors | Shangguang Wang, Ao Zhou |
Place of Publication | Cham |
Publisher | Springer |
Pages | 16-26 |
Number of pages | 11 |
ISBN (Electronic) | 9783319592886 |
ISBN (Print) | 9783319592879 |
DOIs | |
Publication status | Published - 4 Jul 2017 |
Event | 2016 12th EAI International Conference on Collaborative Computing: Networking, Applications and Worksharing - Beijing, China Duration: 12 Nov 2016 → 13 Nov 2016 https://link.springer.com/book/10.1007/978-3-319-59288-6 (Conference proceeding) https://collaboratecom.eai-conferences.org/2016/index.html (Conference website) https://collaboratecom.eai-conferences.org/2016/media/uploads/Collaboratecom%20Final.pdf (Conference program) |
Publication series
Name | Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST |
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Publisher | Springer Cham |
Volume | 201 |
ISSN (Print) | 1867-8211 |
ISSN (Electronic) | 1867-822X |
Conference
Conference | 2016 12th EAI International Conference on Collaborative Computing |
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Country/Territory | China |
City | Beijing |
Period | 12/11/16 → 13/11/16 |
Internet address |
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Scopus Subject Areas
- Computer Networks and Communications
User-Defined Keywords
- Credit evaluation
- Data mining
- Internet finance