PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Mutual Information Based Initialization of Forward-Backward Search for Feature Selection in Regression Problems
Alberto Guillen, Antti Sorjamaa, Gines Rubio, Amaury Lendasse and Ignacio Rojas
In: Artificial Neural Networks – ICANN 2009 Lecture Notes in Computer Science , 5769/2009 . (2009) Springer Berlin / Heidelberg , pp. 1-9. ISBN 0302-9743

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.

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EPrint Type:Book Section
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:6661
Deposited By:Amaury Lendasse
Deposited On:08 March 2010