A theory of multiple classifier systems and its application to visual word recognition
A theory of multiple classifier systems and its application to visual word recognition
Original Contribution: Stacked generalization
Neural Networks
C4.5: programs for machine learning
C4.5: programs for machine learning
Remote Sensing Digital Image Analysis: An Introduction
Remote Sensing Digital Image Analysis: An Introduction
IEEE Transactions on Pattern Analysis and Machine Intelligence
ECML '95 Proceedings of the 8th European Conference on Machine Learning
A Critical Review of Classifier Systems
Proceedings of the 3rd International Conference on Genetic Algorithms
A brief introduction to boosting
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
Parallel consensual neural networks
IEEE Transactions on Neural Networks
Ensembles of Learning Machines
WIRN VIETRI 2002 Proceedings of the 13th Italian Workshop on Neural Nets-Revised Papers
Boosting and Classification of Electronic Nose Data
MCS '02 Proceedings of the Third International Workshop on Multiple Classifier Systems
Empirical characterization of random forest variable importance measures
Computational Statistics & Data Analysis
Semi-supervised multiple classifier systems: background and research directions
MCS'05 Proceedings of the 6th international conference on Multiple Classifier Systems
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The need to optimize the classification accuracy of remotely sensed imagery has led to an increasing use of Earth observation data with different characteristics collected from a variety of sensors from different parts of the electromagnetic spectrum. Combining multisource data is believed to offer enhanced capabilities for the classification of target surfaces. In the paper several single and multiple classifiers which are appropriate for classification of multisource remote sensing and geographic data are considered. The focus is on multiple classifiers: bagging algorithms, boosting algorithms, and consensus theoretic classifiers. These multiple classifiers have different characteristics. The performance of the algorithms in terms of accuracies is compared for a multisource remote sensing and geographic data set.