Abstract
This paper describes an improved version of a previously developed ANOVA and Functional Networks Feature Selection method. This wrapper feature selection method is based on a functional decomposition that grows exponentially as the number of features increases. Since exponential complexity limits the scope of application of the method, a new version is proposed that subdivides this functional decomposition and increases its complexity gradually. The improved version can be applied to a broader set of data. The performance of the improved version was tested against several real datasets. The results obtained are comparable, or better, to those obtained by other standard and innovative feature selection methods.
This work has been funded in part by Project PGIDT05TIC10502PR of the Xunta de Galicia.
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© 2007 Springer-Verlag Berlin Heidelberg
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Sánchez-Maroño, N., Alonso-Betanzos, A., Pérez-Sánchez, B. (2007). An Improved Version of the Wrapper Feature Selection Method Based on Functional Decomposition. In: de Sá, J.M., Alexandre, L.A., Duch, W., Mandic, D. (eds) Artificial Neural Networks – ICANN 2007. ICANN 2007. Lecture Notes in Computer Science, vol 4669. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74695-9_25
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DOI: https://doi.org/10.1007/978-3-540-74695-9_25
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-74693-5
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