A novel supervised feature selection technique based on genetic algorithms

Dealing with a high number of features belonging to different types of data such as Hyperspectral image and Morphological Attribute Profiles (MAPs) might lead to a poor predictive performance of the classifier and hence low final accuracies of classification. This is due to the Hughes effect that consistently decreases the power of prediction of the classifier, in case of a limited and fixed number of training samples. In order to reduce the number of features and only keeping those which are more informative, a novel supervised feature selection technique based on GAs and the measure of the relevance of the features is presented in this work. Moreover, the effectiveness of the proposed technique was demonstrated by experimenting on an optical remote sensed dataset.