Combined SVM-Based Feature Selection and Classification
Feature selection is an important combinatorial optimisation problem in the context of supervised pattern classification. This paper presents four novel continuous feature selection approaches directly minimising the classifier performance. In particular, we include linear and nonlinear Support Vect...
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| Published in: | Machine learning Vol. 61; no. 1-3; pp. 129 - 150 |
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| Main Authors: | , , |
| Format: | Journal Article |
| Language: | English |
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Dordrecht
Springer Nature B.V
01.11.2005
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| ISSN: | 0885-6125, 1573-0565 |
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| Abstract | Feature selection is an important combinatorial optimisation problem in the context of supervised pattern classification. This paper presents four novel continuous feature selection approaches directly minimising the classifier performance. In particular, we include linear and nonlinear Support Vector Machine classifiers. The key ideas of our approaches are additional regularisation and embedded nonlinear feature selection. To solve our optimisation problems, we apply difference of convex functions programming which is a general framework for non-convex continuous optimisation. Experiments with artificial data and with various real-world problems including organ classification in computed tomography scans demonstrate that our methods accomplish the desired feature selection and classification performance simultaneously.[PUBLICATION ABSTRACT] |
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| AbstractList | Feature selection is an important combinatorial optimisation problem in the context of supervised pattern classification. This paper presents four novel continuous feature selection approaches directly minimising the classifier performance. In particular, we include linear and nonlinear Support Vector Machine classifiers. The key ideas of our approaches are additional regularisation and embedded nonlinear feature selection. To solve our optimisation problems, we apply difference of convex functions programming which is a general framework for non-convex continuous optimisation. Experiments with artificial data and with various real-world problems including organ classification in computed tomography scans demonstrate that our methods accomplish the desired feature selection and classification performance simultaneously. Feature selection is an important combinatorial optimisation problem in the context of supervised pattern classification. This paper presents four novel continuous feature selection approaches directly minimising the classifier performance. In particular, we include linear and nonlinear Support Vector Machine classifiers. The key ideas of our approaches are additional regularisation and embedded nonlinear feature selection. To solve our optimisation problems, we apply difference of convex functions programming which is a general framework for non-convex continuous optimisation. Experiments with artificial data and with various real-world problems including organ classification in computed tomography scans demonstrate that our methods accomplish the desired feature selection and classification performance simultaneously.[PUBLICATION ABSTRACT] |
| Author | Steidl, Gabriele Neumann, Julia Schnörr, Christoph |
| Author_xml | – sequence: 1 givenname: Julia surname: Neumann fullname: Neumann, Julia – sequence: 2 givenname: Christoph surname: Schnörr fullname: Schnörr, Christoph – sequence: 3 givenname: Gabriele surname: Steidl fullname: Steidl, Gabriele |
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| References | 1505_CR19 1505_CR18 J. Zhu (1505_CR30) 2004 B. Haasdonk (1505_CR11) 2004 A. Yuille (1505_CR29) 2003; 15 A. Ben-Tal (1505_CR2) 1997; 7 1505_CR15 1505_CR14 1505_CR13 1505_CR12 1505_CR4 1505_CR1 1505_CR5 K. P. Bennett (1505_CR3) 1992; 1 I. Guyon (1505_CR10) 2003; 3 T. Jebara (1505_CR16) 2000 T. Pham Dinh (1505_CR22) 1998; 8 P. S. Bradley (1505_CR6) 1998 G. H. John (1505_CR17) 1994 B. Schölkopf (1505_CR25) 2002 J. Weston (1505_CR27) 2003; 3 1505_CR21 1505_CR24 J. Weston (1505_CR28) 2001 R. T. Rockafellar (1505_CR23) 1970 J. Neumann (1505_CR20) 2004 R. Tibshirani (1505_CR26) 1996; 58 N. Cristianini (1505_CR8) 2002 O. Chapelle (1505_CR7) 1999; 10 R. Duda (1505_CR9) 2000 |
| References_xml | – volume-title: Convex analysis year: 1970 ident: 1505_CR23 doi: 10.1515/9781400873173 – ident: 1505_CR5 – volume: 1 start-page: 23 year: 1992 ident: 1505_CR3 publication-title: Optimization Methods and Software doi: 10.1080/10556789208805504 – volume: 3 start-page: 1157 year: 2003 ident: 1505_CR10 publication-title: Journal of Machine Learning Research – volume: 7 start-page: 347 issue: 2 year: 1997 ident: 1505_CR2 publication-title: SIAM Journal on Optimization doi: 10.1137/S1052623493259215 – ident: 1505_CR18 – start-page: 220 volume-title: Pattern recognition, proc. of 26th DAGM symposium, Vol. 3175 of LNCS year: 2004 ident: 1505_CR11 – start-page: 121 volume-title: Proc. of the 11th international conference on machine learning year: 1994 ident: 1505_CR17 – ident: 1505_CR14 – ident: 1505_CR21 – volume: 3 start-page: 1439 year: 2003 ident: 1505_CR27 publication-title: Journal of Machine Learning Research – ident: 1505_CR12 – start-page: 212 volume-title: Pattern recognition, proc. of 26th DAGM symposium, Vol. 3175 of LNCS year: 2004 ident: 1505_CR20 – volume-title: Advances in neural information processing systems 16 year: 2004 ident: 1505_CR30 – volume: 10 start-page: 1055 issue: 5 year: 1999 ident: 1505_CR7 publication-title: IEEE Transactions on Neural Networks doi: 10.1109/72.788646 – volume-title: Pattern classification year: 2000 ident: 1505_CR9 – volume-title: Learning with kernels year: 2002 ident: 1505_CR25 – ident: 1505_CR4 – ident: 1505_CR24 – start-page: 367 volume-title: Advances in neural information processing systems 14 year: 2002 ident: 1505_CR8 doi: 10.7551/mitpress/1120.003.0052 – ident: 1505_CR1 doi: 10.1145/1015330.1015424 – ident: 1505_CR19 – ident: 1505_CR15 – start-page: 82 volume-title: Proceedings of the 15th international conference on machine learning year: 1998 ident: 1505_CR6 – ident: 1505_CR13 doi: 10.1109/ICPR.2000.906174 – volume: 8 start-page: 476 issue: 2 year: 1998 ident: 1505_CR22 publication-title: SIAM Journal on Optimization doi: 10.1137/S1052623494274313 – start-page: 291 volume-title: Proceedings of the 16th international conference on machine learning year: 2000 ident: 1505_CR16 – volume: 58 start-page: 267 issue: 1 year: 1996 ident: 1505_CR26 publication-title: Journal of the Royal Statistical Society, Series B doi: 10.1111/j.2517-6161.1996.tb02080.x – volume: 15 start-page: 915 year: 2003 ident: 1505_CR29 publication-title: Neural Computation doi: 10.1162/08997660360581958 – start-page: 668 volume-title: Advances in neural information processing systems 13 year: 2001 ident: 1505_CR28 |
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