Please wait a minute...
J4  2010, Vol. 44 Issue (2): 237-242    DOI: 10.3785/j.issn.1008-973X.2010.02.005
    
Preprocess method of pairwise coupling based on multi-spheres
XU Lei, ZHAO Guang-zhou, GU Hong
(College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China)
Download:   PDF(0KB) HTML
Export: BibTeX | EndNote (RIS)      

Abstract  

A preprocess algorithm for pairwise coupling (PWC) decision was proposed to solve the time-consuming problem of training complex dataset of PWC algorithm based on support vector machine (SVM). Multi-sphere (MS) was used to calculate fuzzy memberships of the classes. A set of classes with larger value of fuzzy memberships were picked out as the candidate set for further PWC. Fewer parameters were required with a set of preconditions. The cardinality of the candidate set was evaluated and the parameter search method was given through cross-validation based on the times of kernel evaluations. Simulation results showed that the total decision complexity decreased a lot with a slight loss of accuracy, thus PWC can be applied to quick decisions on complex systems.



Published: 09 March 2010
CLC:  TP 181  
Cite this article:

XU Lei, DIAO Guang-Zhou, GU Hong. Preprocess method of pairwise coupling based on multi-spheres. J4, 2010, 44(2): 237-242.

URL:

http://www.zjujournals.com/eng/10.3785/j.issn.1008-973X.2010.02.005     OR     http://www.zjujournals.com/eng/Y2010/V44/I2/237


成对耦合分类器的多球体预处理方法

为了解决基于支持向量机(SVM)的成对耦合(PWC)决策算法的训练实时性问题,提出一种简化最终决策候选集的预处理方法.通过基于多球体(MS)的简略分类器计算样本与类别间的模糊隶属度,挑选部分隶属度较高的类别用于最终PWC决策.在算法实现方面,通过预设条件简化参数需求,设计基于核函数计算次数的评分函数,并借助交叉验证构造最优参数搜索方法.仿真实验表明,预处理方法以极小的分类正确率损失为代价大大缩短了决策时间,使PWC适用于对决策实时性要求较高的复杂问题.

[1]  VAPNIK V. Statistical learning theory [M]. New York: Wiley, 1998.
[2] KREOEL U. Pairwise classification and support vector machines [C]// Advances in Kernel Methods: Support Vector Learning. Cambridge, MA: MIT, 1999: 255-268.
[3] FRIEDMAN J. Another approach to polychotomous classification [R]. USA: Department of Statistics, Stanford University, 1996.
[4] KNERR S, PERSONNAZ L, DREYFUS G. Single-layer learning revisited: a stepwise procedure for building and training a neural network [C]// Neuro Computing: Algorithms,Architectures and Applications. New York: Springer-Verlag, 1990.
[5] ABE S, INOUE T. Fuzzy support vector machines for multiclass problems [C]// European Symposium on Artificial Neural Networks. Bruges: [s.n.], 2002: 113-118.
[6] PLATT J. Probabilistic outputs for support vector machines and comparison to regularized likelihood methods [C]// Advances in Large Margin Classifiers. Cambridge, MA:MIT, 2000.
[7] HASTIE T, TIBSHIRANI R. Classification by pairwise coupling [J]. The Annals of Statistics, 1998, 26(1): 451-471.
[8] WU T F, LIN C J, WENG R C. Probability estimates for multiclass classification by pairwise coupling [J]. Journal of Machine Learning Research, 2004(5): 975-1005.
[9] CHIANG J H, HAO P Y. A new kernel-based fuzzy clustering approach: support vector clustering with cell growing [J]. IEEE Transactions on Fuzzy Systems, 2003, 11(4):518-527.
[10] HASTIE T, TIBSHIRANI R, FRIEDMAN J. The elements of statistical learning [M]. New York: Springer-Verlag, 2001.
[11] TAX D M J, DUIN R P W. Support vector domain description [J]. Pattern Recognition Letters, 1999, 20(1113): 1191-1199.
[12] BEN-HUR A, HORN D, SIEGELMANN H T, et al. Support vector clustering [J]. Machine Learning Research, 2001(2): 125-137.
[13] PLATT J. Sequential minimal optimization: a fast algorithm for training support vector machines [R]. [S.l.]: Microsoft, 1998.
[14] SCH L B, PLATT J, SHAWE-TAYLOR J, et al. Estimating the support of a high-dimensional distribution [R]. [S.l.]: Microsoft, 1999.

[1] LIN Yi-ning, WEI Wei, DAI Yuan-ming. Semi-supervised Hough Forest tracking method[J]. J4, 2013, 47(6): 977-983.
[2] LI Kan, HUANG Wen-xiong, HUANG Zhong-hua. Multi-sensor detected object classification method based on
support vector machine
[J]. J4, 2013, 47(1): 15-22.
[3] WANG Hong-bo, ZHAO Guang-zhou, QI Dong-lian, LU Da. Fast incremental learning method for one-class support vector machine[J]. J4, 2012, 46(7): 1327-1332.
[4] AI Jie-qing, GAO Ji, PENG Yan-bin, ZHENG Zhi-jun. Negotiation decision model based on transductive
support vector machine
[J]. J4, 2012, 46(6): 967-973.
[5] PAN Jun, KONG Fan-sheng, WANG Rui-qin. Locality sensitive discriminant transductive learning[J]. J4, 2012, 46(6): 987-994.
[6] JIN Zhuo-jun, QIAN Hui, ZHU Miao-liang. Trajectory evaluation method based on intention analysis[J]. J4, 2011, 45(10): 1732-1737.
[7] GU Hong, ZHAO Guang-zhou. Image retrieval and recognition based on generalized
local distance functions
[J]. J4, 2011, 45(4): 596-601.
[8] LUO Jian-hong, CHEN De-zhao. Application of adaptive ensemble algorithm based on
correctness and diversity
[J]. J4, 2011, 45(3): 557-562.
[9] SHANG Xiu-Qin, LEI Jian-Gang, SUN You-Xian. Genetic programming based twoterm prediction model of iron ore burning through point[J]. J4, 2010, 44(7): 1266-1269.