through GRASP Schemata

Of all of the challenges which face the selection of relevant features for predictive data mining or pattern recognition modeling, the adaptation of computational intelligence techniques to feature selection problem requirements is one of the primary impediments. A new improved metaheuristic based on Greedy Randomized Adaptive Search Procedure (GRASP) is proposed for the problem of Feature Selection. Our devised optimization approach provides an eective scheme for wrapper-lter hybridization through the adaptation of GRASP components. The paper investigates, the GRASP component design as well as its adaptation to the feature selection problem. Carried out experiments showed Empirical eectiveness of the devised approach.