Minimising the Delta Test for Variable Selection in Regression Problems
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 Hybridisation 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. Copyright © 2008, Inderscience Publishers.
Recommended Citation
A. Guillén et al., "Minimising the Delta Test for Variable Selection in Regression Problems," International Journal of High Performance Systems Architecture, vol. 1, no. 4, pp. 269 - 281, Inderscience, Jan 2008.
The definitive version is available at https://doi.org/10.1504/IJHPSA.2008.024211
Department(s)
Engineering Management and Systems Engineering
Keywords and Phrases
Delta test; DT; FBS; Forward-backward search; GA; Genetic algorithms; Hybrid algorithms; Parallel architectures; Tabu search; TS; Variable selection
International Standard Serial Number (ISSN)
1751-6536; 1751-6528
Document Type
Article - Journal
Document Version
Final Version
File Type
text
Language(s)
English
Rights
© 2024 Inderscience, All rights reserved.
Creative Commons Licensing
This work is licensed under a Creative Commons Attribution 4.0 License.
Publication Date
01 Jan 2008