Support Vector Machine Classifier With Pinball Loss
Traditionally, the hinge loss is used to construct support vector machine (SVM) classifiers. The hinge loss is related to the shortest distance between sets and the corresponding classifier is hence sensitive to noise and unstable for re-sampling. In contrast, the pinball loss is related to the quan...
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| Published in: | IEEE transactions on pattern analysis and machine intelligence Vol. 36; no. 5; pp. 984 - 997 |
|---|---|
| Main Authors: | , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
Los Alamitos, CA
IEEE
01.05.2014
IEEE Computer Society The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subjects: | |
| ISSN: | 0162-8828, 1939-3539, 2160-9292, 1939-3539 |
| Online Access: | Get full text |
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| Abstract | Traditionally, the hinge loss is used to construct support vector machine (SVM) classifiers. The hinge loss is related to the shortest distance between sets and the corresponding classifier is hence sensitive to noise and unstable for re-sampling. In contrast, the pinball loss is related to the quantile distance and the result is less sensitive. The pinball loss has been deeply studied and widely applied in regression but it has not been used for classification. In this paper, we propose a SVM classifier with the pinball loss, called pin-SVM, and investigate its properties, including noise insensitivity, robustness, and misclassification error. Besides, insensitive zone is applied to the pin-SVM for a sparse model. Compared to the SVM with the hinge loss, the proposed pin-SVM has the same computational complexity and enjoys noise insensitivity and re-sampling stability. |
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| AbstractList | Traditionally, the hinge loss is used to construct support vector machine (SVM) classifiers. The hinge loss is related to the shortest distance between sets and the corresponding classifier is hence sensitive to noise and unstable for re-sampling. In contrast, the pinball loss is related to the quantile distance and the result is less sensitive. The pinball loss has been deeply studied and widely applied in regression but it has not been used for classification. In this paper, we propose a SVM classifier with the pinball loss, called pin-SVM, and investigate its properties, including noise insensitivity, robustness, and misclassification error. Besides, insensitive zone is applied to the pin-SVM for a sparse model. Compared to the SVM with the hinge loss, the proposed pin-SVM has the same computational complexity and enjoys noise insensitivity and re-sampling stability.Traditionally, the hinge loss is used to construct support vector machine (SVM) classifiers. The hinge loss is related to the shortest distance between sets and the corresponding classifier is hence sensitive to noise and unstable for re-sampling. In contrast, the pinball loss is related to the quantile distance and the result is less sensitive. The pinball loss has been deeply studied and widely applied in regression but it has not been used for classification. In this paper, we propose a SVM classifier with the pinball loss, called pin-SVM, and investigate its properties, including noise insensitivity, robustness, and misclassification error. Besides, insensitive zone is applied to the pin-SVM for a sparse model. Compared to the SVM with the hinge loss, the proposed pin-SVM has the same computational complexity and enjoys noise insensitivity and re-sampling stability. Traditionally, the hinge loss is used to construct support vector machine (SVM) classifiers. The hinge loss is related to the shortest distance between sets and the corresponding classifier is hence sensitive to noise and unstable for re-sampling. In contrast, the pinball loss is related to the quantile distance and the result is less sensitive. The pinball loss has been deeply studied and widely applied in regression but it has not been used for classification. In this paper, we propose a SVM classifier with the pinball loss, called pin-SVM, and investigate its properties, including noise insensitivity, robustness, and misclassification error. Besides, insensitive zone is applied to the pin-SVM for a sparse model. Compared to the SVM with the hinge loss, the proposed pin-SVM has the same computational complexity and enjoys noise insensitivity and re-sampling stability. |
| Author | Xiaolin Huang Lei Shi Suykens, Johan A. K. |
| Author_xml | – sequence: 1 surname: Xiaolin Huang fullname: Xiaolin Huang email: huangxl06@mails.tsinghua.edu.cn organization: Dept. of Electr. Eng. (ESAT-STADIUS), Katholieke Univ. Leuven, Leuven, Belgium – sequence: 2 surname: Lei Shi fullname: Lei Shi email: leishi@fudan.edu.cn organization: Dept. of Electr. Eng. (ESAT-STADIUS), Katholieke Univ. Leuven, Leuven, Belgium – sequence: 3 givenname: Johan A. K. surname: Suykens fullname: Suykens, Johan A. K. email: johan.suykens@esat.kuleuven.be organization: Dept. of Electr. Eng. (ESAT-STADIUS), Katholieke Univ. Leuven, Leuven, Belgium |
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| Cites_doi | 10.1093/oso/9780198538493.001.0001 10.1109/IJCNN.2013.6706864 10.1109/TPAMI.2008.225 10.1198/016214507000000617 10.1109/72.950155 10.1142/9789812776655 10.1162/089976602753633411 10.1109/TSMCC.2002.807277 10.1198/016214505000000907 10.1016/j.spl.2004.03.002 10.1017/CBO9780511618796 10.1016/j.ins.2006.03.015 10.1023/A:1018628609742 10.1017/CBO9780511754098 10.3150/10-BEJ267 10.1109/TCSII.2004.824044 10.1016/j.ins.2007.12.012 10.1007/978-1-4757-2440-0 10.1016/j.ins.2007.03.028 |
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| Keywords | pinball loss support vector machine Classification Function evaluation Data analysis Stability Vector support machine Noise immunity Sampling Computational complexity Quantile Signal to noise ratio |
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| References | zhang (ref10) 0 ref13 ref15 xu (ref9) 2009; 10 ref14 ref33 ref11 ref32 zhang (ref29) 2004; 5 lanckriet (ref7) 2003; 3 ref1 steinwart (ref22) 2008 ref16 ref19 ref18 mason (ref23) 0 bi (ref2) 0 christmann (ref20) 2004; 5 suykens (ref31) 1999; 9 yoon (ref12) 0; 3 herbrich (ref4) 0; 2 frank (ref34) 2010 ref24 ref26 ref25 shivaswamy (ref8) 2006; 7 ref21 steinwart (ref17) 0 ref27 guyon (ref3) 1996 ref6 ref5 bishop (ref30) 1995 reid (ref28) 2011; 12 |
| References_xml | – year: 1995 ident: ref30 publication-title: Neural Networks for Pattern Recognition doi: 10.1093/oso/9780198538493.001.0001 – volume: 12 start-page: 731 year: 2011 ident: ref28 article-title: Information, divergence and risk for binary experiments publication-title: J Mach Learn Res – start-page: 181 year: 1996 ident: ref3 article-title: Discovering informative patterns and data cleaning publication-title: Advances in Knowledge Discovery and Data Mining – ident: ref33 doi: 10.1109/IJCNN.2013.6706864 – ident: ref27 doi: 10.1109/TPAMI.2008.225 – start-page: 512 year: 0 ident: ref23 article-title: Boosting algorithms as gradient descent in function space publication-title: Proc NIPS – ident: ref24 doi: 10.1198/016214507000000617 – ident: ref11 doi: 10.1109/72.950155 – ident: ref19 doi: 10.1142/9789812776655 – volume: 7 start-page: 1283 year: 2006 ident: ref8 article-title: Second order cone programming approaches for handling missing and uncertain data publication-title: J Mach Learn Res – ident: ref32 doi: 10.1162/089976602753633411 – ident: ref5 doi: 10.1109/TSMCC.2002.807277 – ident: ref26 doi: 10.1198/016214505000000907 – ident: ref25 doi: 10.1016/j.spl.2004.03.002 – year: 2010 ident: ref34 publication-title: UCI Machine Learning Repository – volume: 3 start-page: 2049 year: 0 ident: ref12 article-title: A role of total margin in support vector machines publication-title: Proc Int Joint Conf Neural Networks – ident: ref21 doi: 10.1017/CBO9780511618796 – ident: ref13 doi: 10.1016/j.ins.2006.03.015 – start-page: 305 year: 0 ident: ref17 article-title: How SVMs can estimate quantiles and the median publication-title: Proc NIPS – start-page: 161 year: 0 ident: ref2 article-title: Support vector classification with input data uncertainty publication-title: Proc NIPS – volume: 9 start-page: 293 year: 1999 ident: ref31 article-title: Least squares support vector machine classifiers publication-title: Neural Process Lett doi: 10.1023/A:1018628609742 – ident: ref16 doi: 10.1017/CBO9780511754098 – ident: ref18 doi: 10.3150/10-BEJ267 – ident: ref6 doi: 10.1109/TCSII.2004.824044 – ident: ref15 doi: 10.1016/j.ins.2007.12.012 – volume: 3 start-page: 555 year: 2003 ident: ref7 article-title: A robust minimax approach to classification publication-title: J Mach Learn Res – volume: 2 start-page: 880 year: 0 ident: ref4 article-title: Adaptive margin support vector machines for classification publication-title: Proc 9th Int Conf Artificial Neural Networks – volume: 5 start-page: 1225 year: 2004 ident: ref29 article-title: Statistical analysis of some multi-category large margin classification methods publication-title: J Mach Learn Res – ident: ref1 doi: 10.1007/978-1-4757-2440-0 – year: 2008 ident: ref22 publication-title: Support Vector Machines – volume: 10 start-page: 1485 year: 2009 ident: ref9 article-title: Robustness and regularization of support vector machines publication-title: J Mach Learn Res – ident: ref14 doi: 10.1016/j.ins.2007.03.028 – volume: 5 start-page: 1007 year: 2004 ident: ref20 article-title: On robustness properties of convex risk minimization methods for pattern recognition publication-title: J Mach Learn Res – start-page: 3 year: 0 ident: ref10 article-title: Using class-center vectors to build support vector machines publication-title: Proc IEEE Signal Processing Soc Workshop Neural Networks for Signal Processing |
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| Snippet | Traditionally, the hinge loss is used to construct support vector machine (SVM) classifiers. The hinge loss is related to the shortest distance between sets... |
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| SubjectTerms | Algorithmics. Computability. Computer arithmetics Algorithms Applied sciences Classification Classifier design and evaluation Classifiers Computer science; control theory; systems Data processing. List processing. Character string processing Exact sciences and technology Fasteners Hinges Kernel Loss measurement Memory organisation. Data processing Noise Optimization Quantiles Regression Robustness Software Support vector machines Theoretical computing |
| Title | Support Vector Machine Classifier With Pinball Loss |
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