An improved version of the wrapper feature selection method based on functional decomposition

Authors:
Noelia Sánchez-Maroño;Amparo Alonso-Betanzos;Beatriz Pérez-Sánchez
Affiliations:
University of A Coruña, Department of Computer Science, A Coruña, Spain;University of A Coruña, Department of Computer Science, A Coruña, Spain;University of A Coruña, Department of Computer Science, A Coruña, Spain
Venue:
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
Year:
2007

Citing 8
Cited 0

Selection of relevant features and examples in machine learning

Artificial Intelligence - Special issue on relevance
Wrappers for feature subset selection

Artificial Intelligence - Special issue on relevance
Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates

Mathematics and Computers in Simulation - IMACS sponsored Special issue on the second IMACS seminar on Monte Carlo methods
Neural Networks for Pattern Recognition

Neural Networks for Pattern Recognition
FS_SFS: A novel feature selection method for support vector machines

Pattern Recognition
A hybrid approach for feature subset selection using neural networks and ant colony optimization

Expert Systems with Applications: An International Journal
Functional network topology learning and sensitivity analysis based on ANOVA decomposition

Neural Computation
Functional networks and analysis of variance for feature selection

IDEAL'06 Proceedings of the 7th international conference on Intelligent Data Engineering and Automated Learning

Quantified Score

Hi-index	0.00

Visualization

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, 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.