Neural networks and the bias/variance dilemma
Neural Computation
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
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
A Unifeid Bias-Variance Decomposition and its Applications
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Diversity-Guided Evolutionary Algorithms
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
A Discussion on the Classifier Projection Space for Classifier Combining
MCS '02 Proceedings of the Third International Workshop on Multiple Classifier Systems
New Measure of Classifier Dependency in Multiple Classifier Systems
MCS '02 Proceedings of the Third International Workshop on Multiple Classifier Systems
Performance Analysis and Comparison of Linear Combiners for Classifier Fusion
Proceedings of the Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
ICDAR '99 Proceedings of the Fifth International Conference on Document Analysis and Recognition
The Knowledge Engineering Review
Neural Networks: A Comprehensive Foundation (3rd Edition)
Neural Networks: A Comprehensive Foundation (3rd Edition)
Comparison of classifier selection methods for improving committee performance
MCS'03 Proceedings of the 4th international conference on Multiple classifier systems
Adaptive combination of adaptive classifiers for handwritten character recognition
Pattern Recognition Letters
EROS: Ensemble rough subspaces
Pattern Recognition
Pareto analysis for the selection of classifier ensembles
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Disturbing Neighbors Ensembles for Linear SVM
MCS '09 Proceedings of the 8th International Workshop on Multiple Classifier Systems
A Labelled Graph Based Multiple Classifier System
MCS '09 Proceedings of the 8th International Workshop on Multiple Classifier Systems
A multidimensional hybrid intelligent method for gear fault diagnosis
Expert Systems with Applications: An International Journal
Selection-fusion approach for classification of datasets with missing values
Pattern Recognition
On the diversity-performance relationship for majority voting in classifier ensembles
MCS'07 Proceedings of the 7th international conference on Multiple classifier systems
On exploration of classifier ensemble synergism in pedestrian detection
IEEE Transactions on Intelligent Transportation Systems
Learn++.MF: A random subspace approach for the missing feature problem
Pattern Recognition
Analysis of bagging ensembles of fuzzy models for premises valuation
ACIIDS'10 Proceedings of the Second international conference on Intelligent information and database systems: Part II
HAIS'11 Proceedings of the 6th international conference on Hybrid artificial intelligent systems - Volume Part II
Making Diversity Enhancement Based on Multiple Classifier System by Weight Tuning
Neural Processing Letters
Using diversity in classifier set selection for arabic handwritten recognition
MCS'10 Proceedings of the 9th international conference on Multiple Classifier Systems
Improving bagging performance through multi-algorithm ensembles
PAKDD'11 Proceedings of the 15th international conference on New Frontiers in Applied Data Mining
Diversity analysis on boosting nominal concepts
PAKDD'12 Proceedings of the 16th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
A competitive ensemble pruning approach based on cross-validation technique
Knowledge-Based Systems
Advanced Engineering Informatics
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Diversity of classifiers is generally accepted as being necessary for combining them in a committee. Quantifying diversity of classifiers, however, is difficult as there is no formal definition thereof. Numerous measures have been proposed in literature, but their performance is often know to be suboptimal. Here several common methods are compared with a novel approach focusing on the diversity of the errors made by the member classifiers. Experiments with combining classifiers for handwritten character recognition are presented. The results show that the approach of diversity of errors is beneficial, and that the novel exponential error count measure is capable of consistently finding an effective member classifier set.