文章基本信息

标题：Credit Scoring via Kernel Matching Pursuit and its Ensemble
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作者：Cuimei Zhang ; Jianwu Li ; Haizhou Wei 等
期刊名称：Advances in Applied Economics and Finance
印刷版ISSN：2167-6348
出版年度：2015
卷号：5
期号：2
页码：787-797
语种：English
出版社：World Science Publisher
摘要：C redit risk is paid more and more attention by financial institutions , and c redit s coring has become an active research topic in computational finance . T his paper proposes to apply k ernel m atching p ursuit (KMP) and its ensemble to credit scoring. KMP originates from m atching p ursuit algorithms that append sequentially basic functions from a basis function dictionary to an initial empty basis using a greedy optimization algorithm, to approximate a given function , and obtain the final solution with a linear combination of chosen functions . KMP is the special m atching p ursuit algorithm using a kernel-based dictionary. An outstanding advantage of KMP in solving classification problems is the sparsity of its solution. Furthermore , we also apply KMP ensemble to credit scoring to model the large-scale data set, which is infeasible for the single KMP . Experimental results based on two data sets f ro m UCI repository and one large data set from individual housing loans in a commercial bank of China show the effectiveness and sparsity of KMP and KMP ensemble in building credit scoring model , compared with the classical classification method － support vector machine .
其他摘要：C redit risk is paid more and more attention by financial institutions , and c redit s coring has become an active research topic in computational finance . T his paper proposes to apply k ernel m atching p ursuit (KMP) and its ensemble to credit scoring. KMP originates from m atching p ursuit algorithms that append sequentially basic functions from a basis function dictionary to an initial empty basis using a greedy optimization algorithm, to approximate a given function , and obtain the final solution with a linear combination of chosen functions . KMP is the special m atching p ursuit algorithm using a kernel-based dictionary. An outstanding advantage of KMP in solving classification problems is the sparsity of its solution. Furthermore , we also apply KMP ensemble to credit scoring to model the large-scale data set, which is infeasible for the single KMP . Experimental results based on two data sets f ro m UCI repository and one large data set from individual housing loans in a commercial bank of China show the effectiveness and sparsity of KMP and KMP ensemble in building credit scoring model , compared with the classical classification method － support vector machine .
关键词：Credit scoring;kernel matching pursuit;kernel matching pursuit ensemble;support vector machine