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.
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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
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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
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