基于模糊粗糙集实例选择的混合算法在信用评分中的应用

doi:10.13190/j.jbupt.2018-178

北京邮电大学学报 ›› 2019, Vol. 42 ›› Issue (2): 101-107.doi: 10.13190/j.jbupt.2018-178

基于模糊粗糙集实例选择的混合算法在信用评分中的应用

刘占峰, 潘甦

南京邮电大学江苏省通信与网络技术工程研究中心, 南京 210003

收稿日期:2018-09-13 出版日期:2019-04-28 发布日期:2019-04-09
通讯作者: 潘甦(1969-),男,教授,博士生导师,E-mail:supan@njupt.edu.cn. E-mail:supan@njupt.edu.cn
作者简介:刘占峰(1980-),男,博士生.
基金资助:
江苏省研究生科研与实践创新计划项目（KYCX18_0882）；南京邮电大学江苏省通信与网络技术工程研究中心开放课题

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

摘要/Abstract

摘要： 基于聚类算法的混合分类器构建的信息评分系统中，不合理的聚类值或者初始类簇中心点会严重影响分类精度的问题，对此，提出了2种基于模糊粗糙集实例选择的新型混合算法.这2种算法仅与数据集的数据结构有关，不受其他外部参数影响.实验结果表明，基于模糊粗糙集实例选择的2种混合算法针对不同结构的数据集表现出了各自的特性，深化了对数据集的理解，提高了准确率.

关键词: 模糊粗糙集实例选择, 混合算法, 信用评分

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

中图分类号:

TP181

刘占峰, 潘甦. 基于模糊粗糙集实例选择的混合算法在信用评分中的应用[J]. 北京邮电大学学报, 2019, 42(2): 101-107.

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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基于模糊粗糙集实例选择的混合算法在信用评分中的应用

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

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