RCGA-S/RCGA-SP Methods to Minimize the Delta Test for Regression Tasks

Authors:
Fernando Mateo;Dušan Sovilj;Rafael Gadea;Amaury Lendasse
Affiliations:
Institute of Applications of Information Technologies and Advanced Communications, Polytechnic University of Valencia, Spain;Laboratory of Information and Computer Science, Helsinki University of Technology, Finland;Institute of Applications of Information Technologies and Advanced Communications, Polytechnic University of Valencia, Spain;Laboratory of Information and Computer Science, Helsinki University of Technology, Finland
Venue:
IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part I: Bio-Inspired Systems: Computational and Ambient Intelligence
Year:
2009

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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.