Rcga-S/rcga-Sp Methods to Minimize the Delta Test for Regression Tasks
Abstract
Frequently, the Number of Input Variables (Features) Involved in a Problem Becomes Too Large to Be Easily Handled by Conventional Machine-Learning Models. This Paper Introduces a Combined Strategy that Uses a Real-Coded Genetic Algorithm to Find the Optimal Scaling (Rcga-S) or Scaling + Projection (Rcga-Sp) Factors that Minimize the Delta Test Criterion for Variable Selection When Being Applied to the Input Variables. These Two Methods Are Evaluated on Five Different Regression Datasets and their Results Are Compared. the Results Confirm the Goodness of Both Methods Although Rcga-Sp Performs Clearly Better Than Rcga-S Because It Adds the Possibility of Projecting the Input Variables Onto a Lower Dimensional Space. © 2009 Springer Berlin Heidelberg.
Recommended Citation
F. Mateo et al., "Rcga-S/rcga-Sp Methods to Minimize the Delta Test for Regression Tasks," Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 5517 LNCS, no. PART 1, pp. 359 - 366, Springer, Aug 2009.
The definitive version is available at https://doi.org/10.1007/978-3-642-02478-8_45
Department(s)
Engineering Management and Systems Engineering
Keywords and Phrases
Delta test; Global search; Input projection; Input scaling; Real-coded genetic algorithm; Variable selection
International Standard Book Number (ISBN)
978-364202477-1
International Standard Serial Number (ISSN)
1611-3349; 0302-9743
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 Springer, All rights reserved.
Publication Date
20 Aug 2009
