The role of feature selection in artifial neural network applications

Abstract

Determination of the 'best' bands that are assigned to the input neurons of an artificial neural network (ANN) is one of the critical steps in designing the ANN for a particular problem. A large number of inputs reduces the network's generalization capabilities and introduces redundant and perhaps irrelevant information, while a small number of inputs could be insufficient for the network to learn the characteristics of the training data. The number of input bands defines the complexity of the problem. Methods used to select the optimum inputs are known as feature selection techniques. Their use in the context of artificial neural networks was investigated in this study. Statistical separability measures, specifically Wilks' Lambda and Hotelling's T-2, and separability indices were employed to determine the best eight-band combination for two multispectral, multitemporal and multisensor image datasets. The Mahalanobis distance classifier was employed in the determination of the 'best' subset solution. In the search for the 'best' band combinations, two widely used search procedures, sequential forward selection and the genetic algorithm, were applied.