Abstract
We explore simultaneous variable subset selection and kernel selection within SVM classification models. First we apply results from SVM classification models with different kernel functions to a fixed subset of credit client variables provided by a German bank. Free variable subset selection for the bank data is discussed next. A simple stochastic search procedure for variable subset selection is also presented.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
GUYON, I. and ELISEFF, A. (2003): An Introduction to Variable and Feature Selection. Journal of Machine Learning Research, 3, 1157–1182.
HASTIE, T., TIBSHIRANI, R. and FRIEDMAN, J. (2001): The Elements of Statistical Learning. Springer, New York.
RAKOTOMAMONJI, A. (2003): Variable Selection Using SVM-based Criteria. Journal of Machine Learning Research, 3, 1357–1370.
SHAWE-TAYLOR, J. and CRISTIANINI, N. (2004): Kernel Methods for Pattern Analysis. Cambridge University Press, Cambridge.
STECKING, R. and SCHEBESCH, K.B. (2003): Support Vector Machines for Credit Scoring: Comparing to and Combining with some Traditional Classification Methods. In: M. Schader, W. Gaul and M. Vichi (Eds.): Between Data Science and Applied Data Analysis. Springer, Berlin, 604–612.
STECKING, R. and SCHEBESCH, K.B. (2006): Comparing and Selecting SVMKernels for Credit Scoring. In: M. Spiliopoulou, R. Kruse, C. Borgelt, A. Nürnberger and W. Gaul (Eds.): From Data and Information Analysis to Knowledge Engineering. Springer, Berlin, 542–549.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Schebesch, K.B., Stecking, R. (2007). Selecting SVM Kernels and Input Variable Subsets in Credit Scoring Models. In: Decker, R., Lenz, H.J. (eds) Advances in Data Analysis. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-70981-7_21
Download citation
DOI: https://doi.org/10.1007/978-3-540-70981-7_21
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-70980-0
Online ISBN: 978-3-540-70981-7
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)