Combination of Multiple Classifiers Using Local Accuracy Estimates
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Experimental evaluation of expert fusion strategies
Pattern Recognition Letters - Special issue on pattern recognition in practice VI
Sum Versus Vote Fusion in Multiple Classifier Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
Interval Data Classification under Partial Information: A Chance-Constraint Approach
PAKDD '09 Proceedings of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
Some Remarks on Chosen Methods of Classifier Fusion Based on Weighted Voting
HAIS '09 Proceedings of the 4th International Conference on Hybrid Artificial Intelligence Systems
Information Sciences: an International Journal
A probabilistic model of classifier competence for dynamic ensemble selection
Pattern Recognition
An experimental study of one- and two-level classifier fusion for different sample sizes
Pattern Recognition Letters
A modal symbolic classifier for interval data
ICONIP'06 Proceedings of the 13th international conference on Neural Information Processing - Volume Part II
One-Class Support Vector Ensembles for Image Segmentation and Classification
Journal of Mathematical Imaging and Vision
Designing classifier fusion systems by genetic algorithms
IEEE Transactions on Evolutionary Computation
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The article presents a new approach of calculating the weight of base classifiers from a committee of classifiers. The obtained weights are interpreted in the context of the interval-valued sets. The work proposes four different ways of calculating weights which consider both the correctness and incorrectness of the classification. The proposed weights have been used in the algorithms which combine the outputs of base classifiers. In this work we use both the outputs, represented by rank and measure level. Research experiments have involved several bases available in the UCI repository and two data sets that have generated distributions. The performed experiments compare algorithms which are based on calculating the weights according to the resubstitution and algorithms proposed in the work. The ensemble of classifiers has also been compared with the base classifiers entering the committee.