The significance of border training patterns in classification by a feedforward neural network using back propagation learning

Training patterns vary in their importance in image classification. Consequently, the selection and refinement of training sets can have a major impact on classification accuracy. For classification by a neural network, training patterns that lie close to the location of decision boundaries in feature space may aid the derivation of an accurate classification. The role of such border training patterns and their identification is discussed in relation to a series of crop classifications from airborne Thematic Mapper data. It is shown that a neural network trained with a set of border patterns mag have a lower accuracy of learning but a significantly higher accuracy of generalization than one trained with a set of patterns drawn from the cores of the classes. Unfortunately, conventional training pattern selection and refinement procedures tend to favour core training patterns. For classification by a neural network, procedures which encourage the inclusion of border training patterns should be adopted as this may facilitate the production of an accurate classification.