The Combination of Evidence in the Transferable Belief Model
IEEE Transactions on Pattern Analysis and Machine Intelligence
Letter Recognition Using Holland-Style Adaptive Classifiers
Machine Learning
Artificial Intelligence
Combining the results of several neural network classifiers
Neural Networks
Three learning phases for radial-basis-function networks
Neural Networks
Hierarchical Object Classification for Autonomous Mobile Robots
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
Solving Multi-class Pattern Recognition Problems with Tree-Structured Support Vector Machines
Proceedings of the 23rd DAGM-Symposium on Pattern Recognition
A new technique for combining multiple classifiers using the dempster-shafer theory of evidence
Journal of Artificial Intelligence Research
Using dempster-shafer theory in MCF systems to reject samples
MCS'05 Proceedings of the 6th international conference on Multiple Classifier Systems
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IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Target Identification from High Resolution Remote Sensing Image by Combining Multiple Classifiers
MCS '09 Proceedings of the 8th International Workshop on Multiple Classifier Systems
Multi-view forests based on Dempster-Shafer evidence theory: a new classifier ensemble method
SPPRA '08 Proceedings of the Fifth IASTED International Conference on Signal Processing, Pattern Recognition and Applications
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Hierarchical neural networks show many benefits when employed for classification problems even when only simple methods analogous to decision trees are used to retrieve the classification result. More complex ways of evaluating the hierarchy output that take into account the complete information the hierarchy provides yield improved classification results. Due to the hierarchical output space decomposition that is inherent to hierarchical neural networks the usage of Dempster-Shafer evidence theory suggests itself as it allows for the representation of evidence at different levels of abstraction. Moreover, it provides the possibility to differentiate between uncertainty and ignorance. The proposed approach has been evaluated using three different data sets and showed consistently improved classification results compared to the simple decision-tree-like retrieval method.