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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Bibliographic Details
Published in:Machine learning Vol. 61; no. 1-3; pp. 129 - 150
Main Authors: Neumann, Julia, Schnörr, Christoph, Steidl, Gabriele
Format: Journal Article
Language:English
Published: Dordrecht Springer Nature B.V 01.11.2005
Subjects:
Optimization
Studies
ISSN:0885-6125, 1573-0565
Online Access:Get full text
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Summary: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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ISSN:0885-6125
1573-0565
DOI:10.1007/s10994-005-1505-9