A Method of Combining Multiple Experts for the Recognition of Unconstrained Handwritten Numerals
IEEE Transactions on Pattern Analysis and Machine Intelligence
Statistical and neural classifiers: an integrated approach to design
Statistical and neural classifiers: an integrated approach to design
An Experimental Comparison of Fixed and Trained Fusion Rules for Crisp Classifier Outputs
MCS '02 Proceedings of the Third International Workshop on Multiple Classifier Systems
Combining Classifiers Based on Minimization of a Bayes Error Rate
ICDAR '99 Proceedings of the Fifth International Conference on Document Analysis and Recognition
Experts' Boasting in Trainable Fusion Rules
IEEE Transactions on Pattern Analysis and Machine Intelligence
k-nearest neighbors directed noise injection in multilayer perceptron training
IEEE Transactions on Neural Networks
Hybrid OCR combination approach complemented by a specialized ICR applied on ancient documents
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
Trainable fusion rules. I. Large sample size case
Neural Networks
Diverse Evolutionary Neural Networks Based on Information Theory
Neural Information Processing
Hybrid Classifier Systems for Intrusion Detection
CNSR '09 Proceedings of the 2009 Seventh Annual Communication Networks and Services Research Conference
Hierarchical behavior knowledge space
MCS'07 Proceedings of the 7th international conference on Multiple classifier systems
Adaptive ROC-based ensembles of HMMs applied to anomaly detection
Pattern Recognition
High performance classifiers combination for handwritten digit recognition
ICAPR'05 Proceedings of the Third international conference on Advances in Pattern Recognition - Volume Part I
Hybrid OCR combination for ancient documents
ICAPR'05 Proceedings of the Third international conference on Advances in Pattern Recognition - Volume Part I
Semi-supervised multiple classifier systems: background and research directions
MCS'05 Proceedings of the 6th international conference on Multiple Classifier Systems
Using an ensemble system to improve concept extraction from clinical records
Journal of Biomedical Informatics
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In the pattern recognition literature, Huang and Suen introduced the "multinomial" rule for fusion of multiple classifiers under the name of Behavior Knowledge Space (BKS) method [1]. This classifier fusion method can provide very good performances if large and representative data sets are available. Otherwise over fitting is likely to occur, and the generalization error quickly increases. In spite of this crucial small sample size problem, analytical models of BKS generalization error are currently not available. In this paper, the generalization error of BKS method is analysed, and a simple analytical model that relates error to sample size is proposed. In addition, a strategy for improving performances by using linear classifiers in "ambiguous" cells of BKS table is described. Preliminary experiments on synthetic and real data sets are reported.