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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.
Abstract 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. [Edit]
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