Abstract-A new feature selection approach that is based on the integration of a genetic algorithm and particle swarm optimization is proposed. The overall accuracy of a support vector machine classifier on validation samples is used as a fitness value. The new approach is carried out on the well-known Indian Pines hyperspectral data set. Results confirm that the new approach is able to automatically select the most informative features in terms of classification accuracy within an acceptable CPU processing time without requiring the number of desired features to be set a priori by users. Furthermore, the usefulness of the proposed method is also tested for road detection. Results confirm that the proposed method is capable of discriminating between road and background pixels and performs better than the other approaches used for comparison in terms of performance metrics. Index Terms-Attribute profile, feature selection, hybridization of genetic algorithm (GA) and particle swarm optimization (PSO), hyperspectral image analysis, road detection, support vector machine (SVM) classifier.
Ghamisi and Benediktsson REF , proposed a feature selection method based on integration of genetic algorithm and particle swarm optimization approaches.
Feature Selection Based on Hybridization of Genetic Algorithm and Particle Swarm Optimization
IEEE Geoscience and Remote Sensing Letters
IEEE Geoscience and Remote Sensing Letters
Mathematics Computer Science