Credit Scoring using Multi-Kernel Support Vector Machine and Chaos Particle Swarm Optimization
Credit Scoring using Multi-Kernel Support Vector Machine and Chaos Particle Swarm Optimization
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DOI：
10.1142/s1469026812500198
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发表时间：
2012-10
期刊：
影响因子：
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通讯作者：
Y. Ling;Qiu-yan Cao;Hua Zhang
中科院分区：
文献类型：
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作者：
Y. Ling;Qiu-yan Cao;Hua Zhang
消费者信用评分被认为是信贷行业的一个关键问题。SVM已成功地用于包括信用评分在内的许多领域的分类。核函数是将支持向量机应用于分类问题以提高预测性能的关键。目前，支持向量机中使用的核函数大多是单核函数，如径向基函数（RBF），它已被广泛使用。在现有核函数的基础上，提出了一种多核函数，通过集成多个单核函数来提高支持向量机的学习和泛化能力。利用一种改进的粒子群算法--混沌粒子群优化算法（CPSO）同时进行参数优化和特征选择。两个UCI信用数据集被用作实验数据，以评估所提出的方法的分类性能。
Consumer credit scoring is considered as a crucial issue in the credit industry. SVM has been successfully utilized for classification in many areas including credit scoring. Kernel function is vital when applying SVM to classification problem for enhancing the prediction performance. Currently, most of kernel functions used in SVM are single kernel functions such as the radial basis function (RBF) which has been widely used. On the basis of the existing kernel functions, this paper proposes a multi-kernel function to improve the learning and generalization ability of SVM by integrating several single kernel functions. Chaos particle swarm optimization (CPSO) which is a kind of improved PSO algorithm is utilized to optimize parameters and to select features simultaneously. Two UCI credit data sets are used as the experimental data to evaluate the classification performance of the proposed method.