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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| Vydáno v: | Machine learning Ročník 61; číslo 1-3; s. 129 - 150 |
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| Hlavní autoři: | , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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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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| Cites_doi | 10.1515/9781400873173 10.1080/10556789208805504 10.1137/S1052623493259215 10.1109/72.788646 10.7551/mitpress/1120.003.0052 10.1145/1015330.1015424 10.1109/ICPR.2000.906174 10.1137/S1052623494274313 10.1111/j.2517-6161.1996.tb02080.x 10.1162/08997660360581958 |
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