Hierarchical mixtures of experts and the EM algorithm
Neural Computation
Machine Learning
Combining Artificial Neural Nets: Ensemble and Modular Multi-Net Systems
Combining Artificial Neural Nets: Ensemble and Modular Multi-Net Systems
Decision boundary feature extraction for neural networks
IEEE Transactions on Neural Networks
Adaptive Feature Spaces for Land Cover Classification with Limited Ground Truth Data
MCS '02 Proceedings of the Third International Workshop on Multiple Classifier Systems
Multiclassifier Systems: Back to the Future
MCS '02 Proceedings of the Third International Workshop on Multiple Classifier Systems
Configurable Hybrid Architectures for Character Recognition Applications
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
Target Identification from High Resolution Remote Sensing Image by Combining Multiple Classifiers
MCS '09 Proceedings of the 8th International Workshop on Multiple Classifier Systems
Design of hierarchical classifier with hybrid architectures
PReMI'05 Proceedings of the First international conference on Pattern Recognition and Machine Intelligence
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Many real world classification problems involve high dimensional inputs and a large number of classes. Feature extraction and modular learning approaches can be used to simplify such problems. In this paper, we introduce a hierarchical multiclassifier paradigm in which a C- class problem is recursively decomposed into C - 1 two-class problems. A generalized modular learning framework is used to partition a set of classes into two disjoint groups called meta-classes. The coupled problem of finding a good partition and of searching for a linear feature extractor that best discriminates the resulting two meta-classes are solved simultaneously at each stage of the recursive algorithm. This results in a binary tree whose leaf nodes represent the original C classes. The proposed hierarchical multiclassifier architecture was used to classify 12 types of landcover from 183-dimensional hyperspectral data. The classification accuracy was significantly improved by 4 to 10% relative to other feature extraction and modular learning approaches. Moreover, the class hierarchy that was automatically discovered conformed very well with a human domain expert's opinion, which demonstrates the potential of such a modular learning approach for discovering domain knowledge automatically from data.