PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

New methodologies based on delta test for variable selection in regression problems
Alberto Guillén, Dusan Sovilj, Fernando Mateo, Fernando Mateo and Amaury Lendasse
In: Workshop on Parallel Architectures and Bioinspired Algorithms, October 25-29, 2008, Toronto, Canada.


The problem of selecting an adequate set of variables from a given data set of a sampled function, becomes crucial by the time of designing the model that will approximate it. Several approaches have been presented in the literature although recent studies showed how the Delta Test is a powerful tool to determine if a subset of variables is correct. This paper presents new methodologies based on the Delta Test such as Tabu Search, Genetic Algorithms and the hybridization of them, to determine a subset of variables which is representative of a function. The paper considers as well the scaling problem where a relevance value is assigned to each variable. The new algorithms were adapted to be run in parallel architectures so better performances could be obtained in a small amount of time, presenting great robustness and scalability.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:4780
Deposited By:Amaury Lendasse
Deposited On:24 March 2009