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Soft Computing

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01.09.2010 | Original Paper

Robust least squares support vector machine based on recursive outlier elimination

verfasst von: Wen Wen, Zhifeng Hao, Xiaowei Yang

Erschienen in: Soft Computing | Ausgabe 11/2010

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Abstract

To achieve robust estimation for noisy data set, a recursive outlier elimination-based least squares support vector machine (ROELS-SVM) algorithm is proposed in this paper. In this algorithm, statistical information from the error variables of least squares support vector machine is recursively learned and a criterion derived from robust linear regression is employed for outlier elimination. Besides, decremental learning technique is implemented in the recursive training–eliminating stage, which ensures that the outliers are eliminated with low computational cost. The proposed algorithm is compared with re-weighted least squares support vector machine on multiple data sets and the results demonstrate the remarkably robust performance of the ROELS-SVM.

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Brabanter JD (2004) LS-SVM regression modelling and its applications. Ph.D. thesis. ftp://ftp.esat.kuleuven.ac.be/pub/SISTA//debrabant

Burges CJC (1998) A tutorial on support vector machines for pattern recognition. Data Min Knowl Discov 2(2):955–974CrossRef

Cao LJ, Tay FEH (2003) Support vector machine with adaptive parameters in financial time series forecasting. IEEE Trans Neural Netw 14(6):1506–1518CrossRef

Cawley GC, Talbot NLC (2004) Fast exact leave-one-out cross-validation of sparse least-squares support vector machines. Neural Netw 17:1467–1475MATHCrossRef

Chuang CC, Su SF, Jeng JT, Hsiao CC (2002) Robust support vector regression networks for function approximation with outliers. IEEE Trans Neural Netw 13(6):1322–1330CrossRef

Cortes C (1993) Prediction of generalization ability in learning machines. Ph.D. thesis. http://homepage.mac.com/corinnacortes/

Demsar J (2006) Statistical comparisons of classifiers over multiple data sets. J Mach Learn Res 7:1–30MathSciNet

Evgeniou T, Pontil M, Poggio T (2000) Regularization networks and support vector machines. Adv Comput Math 13:1–50MATHCrossRefMathSciNet

Grubbs FE (1969) Procedures for detecting outlying observations in samples. Technometrics 11(1):1–21CrossRef

Jiang JQ, Song CY, Wu CG, Maurizio M, Liang YC (2006) Support vector machine regression algorithm based on chunking incremental learning. In: Proceedings of ICCS’06. Lecture notes in computer science, vol 3991. Springer, Berlin, pp 547–554

Kvalseth TO (1985) Cautionary Note about R ². Am Stat 39(4):279–285CrossRef

Mangasarian OL, Musicant DR (2000) Robust linear and support vector regression. IEEE Trans Pattern Anal Mach Intell 22(9):950–955CrossRef

Rousseeuw PJ (1984) Least median of squares regression. J Am Stat Assoc 79:871–880MATHCrossRefMathSciNet

Rousseeuw PJ, Driessen KV (2006) Computing LTS Regression for large data sets. Data Min Knowl Discov 12:29–45CrossRefMathSciNet

Rousseeuw PJ, Leroy A (1987) Robust regression and outlier detection. Wiley, New York, pp 9–11MATHCrossRef

Scholkopf B, Sung KK, Burges CJC, Girosi F, Niyogi P, Poggio T, Vapnik V (1997) Comparing support vector machines with Gaussian kernels to radial basis function classifiers. IEEE Trans Signal Process 45(11):2758–2765CrossRef

Smola AJ, Scholkopf B (1998) A tutorial on support vector regression. NeuroCOLT2 Technical Report NC2-TR-1998-030

Suykens JAK, Vandewalle J (1999) Least squares support vector machine classifiers. Neural Process Lett 9:293–300CrossRefMathSciNet

Suykens JAK, Brabanter JD, Lukas L, Vandewalle J (2002) Weighted least squares support vector machines: robustness and sparse approximation. Neurocomputing 48:85–105MATHCrossRef

Tian SF, Huang HK (2002) A simplification algorithm to support vector machines for regression. J Softw 13(6):1169–1172

Vapnik V (1995) The nature of statistical learning theory. Wiley, New YorkMATH

Wen W, Hao ZF, Yang XW (2008) A heuristic weight-setting strategy and iteratively updating algorithm for weighted least-squares support vector regression. Neurocomputing 71(16–18):3096–3103CrossRef

Wu CH (2004) Travel-time prediction with support vector regression. IEEE Trans Intell Transp Syst 5(4):276–281CrossRef

Zhang JS, Gao G (2005) Reweighted robust support vector regression method. Chin J Comput Sci 28(7):1171–1177MathSciNet

Zhao Y, Keong KC (2004) Fast Leave-one-out Evaluation and improvement on inference for LS-SVMs. In: Proceedings of ICPR’04, vol 3, pp 494–497

Titel: Robust least squares support vector machine based on recursive outlier elimination
verfasst von: Wen Wen
Zhifeng Hao
Xiaowei Yang
Publikationsdatum: 01.09.2010
Verlag: Springer-Verlag
Erschienen in: Soft Computing / Ausgabe 11/2010
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI: https://doi.org/10.1007/s00500-009-0535-9

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