Small Sample Size Effects in Statistical Pattern Recognition: Recommendations for Practitioners
IEEE Transactions on Pattern Analysis and Machine Intelligence
Network generalization differences quantified
Neural Networks
The Random Subspace Method for Constructing Decision Forests
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Combination Fingerprint Classifier
IEEE Transactions on Pattern Analysis and Machine Intelligence - Graph Algorithms and Computer Vision
Complexity Measures of Supervised Classification Problems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Feature Extraction Based on Decision Boundaries
IEEE Transactions on Pattern Analysis and Machine Intelligence
Data Complexity Analysis for Classifier Combination
MCS '01 Proceedings of the Second International Workshop on Multiple Classifier Systems
Pretopological Approach for Supervised Learning
ICPR '96 Proceedings of the International Conference on Pattern Recognition (ICPR '96) Volume IV-Volume 7472 - Volume 7472
On the Nonlinearity of Pattern Classifiers
ICPR '96 Proceedings of the International Conference on Pattern Recognition (ICPR '96) Volume IV-Volume 7472 - Volume 7472
Engineering multiversion neural-net systems
Neural Computation
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Classifier selection is an important step in designing multiple classifier systems. In this paper classifier geometrical characteristic comparison methods including volume occupation difference comparison and decision boundary curvature difference comparison are proposed. Based on the comparison which is directly carried out on training samples, difference between classifiers can be measured. The usefulness of these methods to measure the difference between classifiers' decision space and their influence on recognition rate of multiple classifier systems are confirmed by a series of experiments. Then a classifier selection strategy based on these comparison methods is introduced. The experiment results show that it is a useful selection strategy for selecting classifiers to combine.