Mutual Information based Initialization of Forward-Backward Search for Feature Selection in Regression Problems
Abstract
Pure Feature Selection, Where Variables Are Chosen or Not to Be in the Training Data Set, Still Remains as an Unsolved Problem, Especially When the Dimensionality is High. Recently, the Forward-Backward Search Algorithm using the Delta Test to Evaluate a Possible Solution Was Presented, Showing a Good Performance. However, Due to the Locality of the Search Procedure, the Initial Starting Point of the Search Becomes Crucial in Order to Obtain Good Results. This Paper Presents New Heuristics to Find a More Adequate Starting Point that Could Lead to a Better Solution. the Heuristic is based on the Sorting of the Variables using the Mutual Information Criterion, and Then Performing Parallel Local Searches. These Local Searches Provide an Initial Starting Point for the Actual Parallel Forward-Backward Algorithm. © 2009 Springer Berlin Heidelberg.
Recommended Citation
A. Guillén et al., "Mutual Information based Initialization of Forward-Backward Search for Feature Selection in Regression Problems," Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 5768 LNCS, no. PART 1, pp. 1 - 9, Springer, Nov 2009.
The definitive version is available at https://doi.org/10.1007/978-3-642-04274-4_1
Department(s)
Engineering Management and Systems Engineering
International Standard Book Number (ISBN)
978-364204273-7
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
06 Nov 2009
