A novel GA-based feature selection approach for high dimensional data

In this paper we present a novel GA-based approach for feature selection in high dimensional spaces. The proposed system is able to greatly reduce the number of features to be used in the classification phase and can deal with problems involving thousands of features. The system is based on two modules. The first module employs a feature ranking method to reduce the number of features to be taken into account. The second module uses a GA-based search strategy that uses a filter fitness function for finding feature subsets with a high discriminative power