Genetic algorithm for feature selection for parallel classifiers
Information Processing Letters
A Method of Combining Multiple Experts for the Recognition of Unconstrained Handwritten Numerals
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Random Subspace Method for Constructing Decision Forests
IEEE Transactions on Pattern Analysis and Machine Intelligence
Strategies for combining classifiers employing shared and distinct pattern representations
Pattern Recognition Letters - special issue on pattern recognition in practice V
MCS '00 Proceedings of the First International Workshop on Multiple Classifier Systems
Experiments with Classifier Combining Rules
MCS '00 Proceedings of the First International Workshop on Multiple Classifier Systems
Ensemble Methods in Machine Learning
MCS '00 Proceedings of the First International Workshop on Multiple Classifier Systems
Designing classifier fusion systems by genetic algorithms
IEEE Transactions on Evolutionary Computation
Ensembles of Learning Machines
WIRN VIETRI 2002 Proceedings of the 13th Italian Workshop on Neural Nets-Revised Papers
Decision Level Fusion of Intramodal Personal Identity Verification Experts
MCS '02 Proceedings of the Third International Workshop on Multiple Classifier Systems
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A classifier team is used in preference to a single classifier in the expectation it will be more accurate. Here we study the potential for improvement in classifier teams designed by the feature subspace method: the set of features is partitioned and each subset is used by one classifier in the team. All partitions of a set of 10 features into 3 subsets containing 〈4; 4; 2〉 features and 〈4; 3; 3〉 features, are enumerated and nine combination schemes are applied on the three classifiers. We look at the distribution and the extremes of the improvement (or failure); the chances of the team outperforming the single best classifier if the feature space is partitioned at random; the relationship between the spread of the individual classifier accuracy and the team accuracy; and the combination schemes performance.