Evolutive Approaches for Variable Selection using a Non-Parametric Noise Estimator
Abstract
The Design of a Model to Approximate a Function Relies Significantly on the Data Used in the Training Stage. the Problem of Selecting an Adequate Set of Variables Should Be Treated Carefully Due to its Importance. If the Number of Variables is High, the Number of Samples Needed to Design the Model Becomes Too Large and the Interpretability of the Model is Lost. This Chapter Presents Several Methodologies to Perform Variable Selection in a Local or a Globalmanner using a Non-Parametric Noise Estimator to Determine the Quality of a Subset of Variables. Several Methods that Apply Parallel Paradigms in Different Architecures Are Compared from the Optimization and Efficiency Point of View Since the Problem is Computationally Expensive. © 2012 Springer Berlin Heidelberg.
Recommended Citation
A. Guillén et al., "Evolutive Approaches for Variable Selection using a Non-Parametric Noise Estimator," Studies in Computational Intelligence, vol. 415, pp. 243 - 266, Springer, Jan 2012.
The definitive version is available at https://doi.org/10.1007/978-3-642-28789-3_11
Department(s)
Engineering Management and Systems Engineering
International Standard Book Number (ISBN)
978-364228788-6
International Standard Serial Number (ISSN)
1860-949X
Document Type
Article - Journal
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 Springer, All rights reserved.
Publication Date
01 Jan 2012
