On the estimation of spatial-spectral mixing with classifier likelihood functions
Pattern Recognition Letters - Special issue on non-conventional pattern analysis in remote sensing
Pattern Recognition Letters - Special issue on non-conventional pattern analysis in remote sensing
A fuzzy set-based accuracy assessment of soft classification
Pattern Recognition Letters
Pattern Recognition Letters
Application of majority voting to pattern recognition: an analysis of its behavior and performance
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Combinations of weak classifiers
IEEE Transactions on Neural Networks
FaSS: Ensembles for Stable Learners
MCS '09 Proceedings of the 8th International Workshop on Multiple Classifier Systems
Using uncertainty information to combine soft classifications
IPMU'10 Proceedings of the Computational intelligence for knowledge-based systems design, and 13th international conference on Information processing and management of uncertainty
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Although soft classification analyses can reduce problems such as those associated with mixed pixels that impact negatively on conventional hard classifications their accuracy is often low. One approach to increasing the accuracy of soft classifications is the use of an ensemble of classifiers, an approach which has been successful for hard classifications but rarely applied for soft classifications. Four methods for combining soft classifications to increase soft classification accuracy were assessed. These methods were based on (i) the selection of the most accurate predictions on a class-specific basis, (ii) the average of the outputs of the individual classifications for each case, (iii) the direct combination of classifications using evidential reasoning and (iv) the adaptation of the outputs to enable the use of a conventional (hard classification) ensemble approach. These four approaches were assessed with classifications of National Oceanic and Atmospheric Administration (NOAA) Advanced Very High-Resolution Radiometer (AVHRR) imagery of Australia. The data were classified using two neural networks and a probabilistic classifier. All four ensemble approaches applied to the outputs of these three classifiers were found to increase classification accuracy. Relative to the most accurate individual classification, the increases in overall accuracy derived ranged from 2.20% to 4.45%, increases that were statistically significant at 95% level of confidence. The results highlight that ensemble approaches may be used to significantly increase soft classification accuracy.