Effective Wrapper-Filter hybridization through GRASP Schemata


Mohamed Amir Esseghir ;
Proceedings of the Fourth International Workshop on Feature Selection in Data Mining, PMLR 10:45-54, 2010.


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 \textitGreedy Randomized Adaptive Search Procedure (GRASP) is proposed for the problem of Feature Selection. Our devised optimization approach provides an effective scheme for wrapper-filter 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 effectiveness of the devised approach.

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