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基于改进的SVM学习算法及其在信用评分中的应用 被引量：21

An improved SVM learning algorithm and its applications to credit scorings
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摘要 对于处理大规模问题的信用评分方法除要求达到一定的准确率之外,其速度、可解释性、简洁性等性能也非常重要.借鉴SMO的思想,首先提出一个基于三变量的改进的SVM学习算法,即将SVM问题分解为一系列含有三个变量的二次规划子问题,其优点是所求的相应松弛子问题都有解析解,使得该方法能够更加精确和快速地逼近最优解;其次将新算法应用于信用评分问题,在UCI机器学习库中的三个公共数据集上的数值试验表明了新方法的有效性:不仅节省了模型的计算代价,而且还提高了分类精度. A credit scoring method for a large problem not only achieves a certain its speed, interpretability, simplicity and other performance are also very important accuracy In this paper, a novel method called an improved SVM learning algorithm based on three-variable working set （ISVM-TV） is presented. This algorithm is derived by solving a series of the QP problems with only three points and the corresponding relaxation subproblems are solved analytically so that the proposed method approaches to the optimal solution more quickly. The proposed method is introduced to credit scoring and three datasets from UCI machine learning datasets are selected to demonstrate the method＇s competitive performance. Moreover, ISVM-TV shows a superior performance in saving the computational cost and improving classification accuracy.
作者 陆爱国 王珏 刘红卫
机构地区 西安电子科技大学理学院 中国科学院数学与系统科学研究院预测科学研究中心
出处 《系统工程理论与实践》 EI CSSCI CSCD 北大核心 2012年第3期515-521,共7页 Systems Engineering-Theory & Practice
基金 国家自然科学基金(70801058 70971052 61072144)
关键词 支持向量机 三变量工作集 序列最小优化法 最大违背对 信用评分 support vector machine （SVM） three-variable working set sequential minimal optimization（SMO） maximal violating pair （MVP） credit scoring
分类号 TP181 [自动化与计算机技术—控制理论与控制工程]
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参考文献15

  • 1Desai V S, Crook J N, Overstreet G A. A comparison of neural network and linear scoring models in the credit union environment[J]. European Journal of Operational Research, 1996, 95(1): 24-37.
  • 2Lacerda E, Carvalho A C P L F, Braga A P, et al. Evolutionary radial basis functions for credit assessment[J]. Applied Intelligence, 2005, 22(3): 167 -181.
  • 3Laitinen E K. Predicting a corporate credit analyst's risk estimate by logistic and linear models[J]. International Review of Financial Analysis, 1999, 8(2): 97 -121.
  • 4Huang C L, Chen M C, Wang C J. Credit scoring with a data mining approach based on support vector machines[J]. Expert Systems with Applications, 2007, 33(4): 847-856.
  • 5Thomas L C, Edelman D B, Crook J N. A survey of the issues in consumer credit modeling research[J]. Journal of the Operational Research Society, 2005, 56(9): 1006 -1015.
  • 6Vapnik V N. The Nature of Statistical Learning Theory[M]. Berlin: Springer, 1995.
  • 7Boser B, Guyon I, Vapnik V N. A training algorithm for optimal margin classifiers[C]// Proceedings of the Fifth Annual Workshop on Computational Learning Theory, Pittsburgh, PA: ACM, 1992:144- 152.
  • 8Osuna E, Pretmd R, Gimsi F. An improved training algorithm for support vector machines[C]// Proceedings of the 1997 IEEE Workshop on Neural Networks for Signal Processing. New York: IEEE Press, 1997: 276-285.
  • 9Osuna E, Pretmd R, Gimsi F. Training support vector machines: An application to face detection[C]// IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'97), 1997:130 -136.
  • 10Platt J. Sequential minimal optimization: A fast algorithm for training support vector machines[R]. Technical Report, 1998.

二级参考文献10

  • 1Vapnik V N. The Nature of Statistical Learning Theory[M]. Berlin: Springer, 1995.
  • 2Boser B, Guyon I, Vapnik V N. A training algorithm for optimal margin classifiers[C]//Proceedings of the Fifth Annual Workshop on Computational Learning Theory. Pittsburgh, PA: ACM, 1992: 144-152.
  • 3Osuna E, Fretmd R, Gimsi F. An improved training algorithm for support vector machines[C]//Proceedings of the 1997 IEEE Workshop on Neural Networks for Signal Processing, New York: IEEE Press, 1997:276 285.
  • 4Osuna E, Pretmd R, Gimsi F. Training support vector machines: An application to face detection[C]//1997 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'97), 1997:130 136.
  • 5Platt J. Sequential minimal optimization: A fast algorithm for training support vector machines[R]. Microsoft Research, Technical Report MSR-TR-98-14, 1998.
  • 6Platt J. Fast Training of Support Vector Machines Using Sequential Minimal Optimization[M]//Scholkopf B, Burges C, Smola A. Advance in Kernel Methods: Support Vector Learning, Cambridge, MA: MIT Press, 1998: 185-208.
  • 7Platt J. Using analytic QP and sparseness to speed training of support vector machines[C]//Proceedings of the 1998 Conference on Advances in Neural Information Processing Systems II. Cambridge, MA: MIT Press, 1998: 557-563.
  • 8Chen P H, Fan R E, Lin C J. A study on SMO-type decomposition methods for support vector machines[J]. IEEE Transactions on Neural Networks, 2006, 17(4): 893-908.
  • 9Keerthi S, Shevade S K, Bhattacharyya C, et al. Improvements to Platt's SMO algorithm for SVM classifier design[J]. Neural Computation, 2001, 13: 637-649.
  • 10Joachims T. Making Large-Scale SVM Learning Practical[M]//Scholkopf B, Burges C, Smola A. Advance in Kernel Methods: Support Vector Learning, Cambridge, MA: MIT Press, 1998: 169-185.

共引文献1

同被引文献183

引证文献21

二级引证文献134

系统工程理论与实践
2012年 第3期

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