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

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. Key words: real-coded genetic algorithm, global search, variable selection, delta test, input scaling, input projection

• Type: Conference
• Year: 2009
• Where: IWANN
• Authors: Fernando Mateo, Dusan Sovilj, Rafael Gadea Gironés, Amaury Lendasse
• Keywords: Artificial Intelligence, Input Variables, IWANN 2009, Real-coded Genetic Algorithm, Variable Selection