Title :
Comparative Study on Class Imbalance Learning for Credit Scoring
Author_Institution :
Sch. of Econ. & Manage., Heilongjiang Inst. of Sci. & Technol., Harbin, China
Abstract :
This paper performs systematic comparative studies on weighted methods including weight C4.5, weighted SVM and weighted rough set with traditional C4.5, SVM and rough set for credit scoring. The experiments show that the weighted methods outperform to the traditional methods when the methods are sensitive to the class distribution.
Keywords :
decision trees; learning (artificial intelligence); rough set theory; support vector machines; class imbalance learning; credit scoring; weight C4.5; weighted SVM; weighted methods; weighted rough set; Conference management; Costs; Decision trees; Hybrid intelligent systems; Paper technology; Risk management; Support vector machine classification; Support vector machines; Technology management; Training data; C4.5; Rough Set; SVM; class imbalance learning; credit scoring; weight Rough Set; weighted C4.5; weighted SVM;
Conference_Titel :
Hybrid Intelligent Systems, 2009. HIS '09. Ninth International Conference on
Conference_Location :
Shenyang
Print_ISBN :
978-0-7695-3745-0
DOI :
10.1109/HIS.2009.133
