A new variable selection method for classification

This work proposes an “ad hoc” new method for variable selection in classification, specifically in Discriminant Analysis. This new method is based on the metaheuristic strategy Tabu Search. From a computational point of view variable selection is a NP-Hard problem and therefore there is no guarantee of finding the optimum solution (NP = Nondeterministic Polynomial Time). This means that when the size of the problem is large finding an optimum solution in practice is unfeasible. As found in other optimization problems, metaheuristic techniques have proved to be good at solving this type of problems. Although there are many references in the literature regarding selecting variables for their use in classification, there are very few key references on the selection of variables for their use in Discriminant Analysis. In fact, the most well-known statistical packages continue to use classic selection methods as Stepwise, Backward or Forward. After performing some tests it is found that Tabu Search obtains significantly better results than the Stepwise, Backward or Forward methods used by classic statistical packages.