Hybrid Algorithm Base on Fuzzy-Rough Instance Selection for Credit Scoring

doi:10.13190/j.jbupt.2018-178

JOURNAL OF BEIJING UNIVERSITY OF POSTS AND TELECOM ›› 2019, Vol. 42 ›› Issue (2): 101-107.doi: 10.13190/j.jbupt.2018-178

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Hybrid Algorithm Base on Fuzzy-Rough Instance Selection for Credit Scoring

LIU Zhan-feng, PAN Su

Jiangsu Engineering Research Center of Communication and Network Technology, Nanjing University of Posts and Telecommunications, Nanjing 210003, China

Received:2018-09-13 Online:2019-04-28 Published:2019-04-09

Abstract

Abstract: For the credit scoring system built on cluster algorithm based hybrid classifier, the unreasonable clusters number or starting center points of each cluster have severely negative influence on the classification accuracy. In order to solve the problem, two new hybrid algorithms based on fuzzy-rough instance selection were proposed respectively, which are only related to intrinsic data structure of datasets and are not affected by other external parameters. The experimental results show that the proposed hybrid algorithms exhibit their own characteristics for datasets with different structures, which deepens the understanding of data sets and improves the accuracy.

Key words: fuzzy-rough instance selection, hybrid algorithm, credit scoring

CLC Number:

TP181

LIU Zhan-feng, PAN Su. Hybrid Algorithm Base on Fuzzy-Rough Instance Selection for Credit Scoring[J]. JOURNAL OF BEIJING UNIVERSITY OF POSTS AND TELECOM, 2019, 42(2): 101-107.

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Hybrid Algorithm Base on Fuzzy-Rough Instance Selection for Credit Scoring

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