Increasing soft classification accuracy through the use of an ensemble of classifiers

Three possible methods of combining soft classification outputs to increase soft classification accuracy were assessed. These methods were (i) an approach that selects the most accurate predictions on a class-specific basis, (ii) Dempster-Shafer theory of evidence and (iii) an approach which degrades the soft classification output into a set of ordered classes and then combines these through the use of a conventional ensemble approach. The potential of these approaches was assessed using coarse spatial resolution NOAA AVHRR imagery of Australia. The data were classified using two neural networks (a multi-layer perceptron and a radial basis function network) as well as a probabilistic classifier. All three approaches to combine the classifications were applied to combine the soft classification outputs and had been shown to increase classification accuracy. Relative to the most accurate individual classification, the increases in overall accuracy derived ranged from 2.73 to 4.45% and large increases in individual class accuracy were also observed. The results highlight that ensemble based approaches may be used to increase soft classification accuracy.