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
Artificial Neural Networks – ICANN 2009
Lecture Notes in Computer Science
Springer Berlin / Heidelberg
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.