Hybrid genetic algorithm for feature selection with hyperspectral data

Abstract

This letter evaluates the performance of a wrapper–filter genetic algorithm (GA) for feature selection using Digital Airborne Imaging Spectrometer hyperspectral data set. Classification accuracy by k-nearest neighbour (k-NN) and support vector machine (SVM) were used as fitness function with four filter algorithms. Results of wrapper–filter GA-based feature selection approach were compared with a steady-state GA-based approach in terms of number of selected features and the accuracy with selected feature. Result indicates the unsuitability of SVM as wrapper due to large computational cost in comparison to k-NN. Choice of filter algorithm seems to be having no significant effect on classification accuracy obtained using selected features. Comparison in terms of number of selected features and the accuracy with selected features suggests comparable performance by both wrapper–filter and steady-state GAs for the used data set.