计算机科学与探索Issue(5)：473-480,8.DOI:10.3778/j.issn.1673-9418.2012.05.009
基于改进图半监督学习的个人信用评估方法
Personal Credit Scoring Method Using Improved Graph Based Semi-Supervised Learning
摘要
Abstract
Labeled instances are expensive to collect for personal credit scoring. However, unlabeled data are often relatively easy to obtain. Aiming at this problem and the ubiquitous asymmetry of credit datasets, this paper proposes a personal credit scoring model based on improved graph based semi-supervised learning method. Because the model adopts semi-supervised technology, it can learn from abundant unlabeled instances to avoid the decreasing of generalization ability which is induced by the relative lack of labeled data. Furthermore, by improving graph based semi-supervised learning technology with normalization and modification of decision boundary on its iterative results, the scoring model effectively reduces the bad impact of asymmetric dataset. Experiments on three UCI credit approval datasets show that the new scoring model can provide significantly better results than support vector machines and the unimproved method.关键词
信用评估/支持向量机/图半监督学习/不对称数据集Key words
credit scoring/ support vector machine/ graph based semi-supervised learning/ asymmetric dataset分类
信息技术与安全科学引用本文复制引用
张燕,张晨光,张夏欢..基于改进图半监督学习的个人信用评估方法[J].计算机科学与探索,2012,(5):473-480,8.基金项目
The College Scientific Research Program of Education Department of Hainan Province under Grant No.Hjkj2012-01(海南省教育厅高等学校科学研究项目). （海南省教育厅高等学校科学研究项目）
