A novel hybrid data mining framework for credit evaluation

Yatao YANG, Zibin ZHENG, Chunzhen HUANG, Kunmin LI, Hong Ning DAI*

*Corresponding author for this work

Research output: Book Chapters | Papers in Conference ProceedingsBook ChapterResearchpeer-review

1 Citation (Scopus)

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 languageEnglish
Title of host publicationLecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST
PublisherSpringer-Verlag GmbH and Co. KG
Pages16-26
Number of pages11
DOIs
Publication statusPublished - Jul 2017
Externally publishedYes

Publication series

NameLecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST
Volume201
ISSN (Print)1867-8211

Bibliographical note

Funding Information:
The work described in this paper was supported by the National Key Research and Development Program (2016YFB1000101), the National Natural Science Foundation of China under (61472338), the Fundamental Research Funds for the Central Universities, and Macao Science and Technology Development Fund under Grant No. 096/2013/A3.

Publisher Copyright:
© 2017, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

Keywords

  • Credit evaluation
  • Data mining
  • Internet finance

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