Combination of Multiple Classifiers Using Local Accuracy Estimates
IEEE Transactions on Pattern Analysis and Machine Intelligence
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Machine Learning
Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
Performance and Diversity Evaluation in Hybrid and Non-Hybrid Structures of Ensembles
HIS '05 Proceedings of the Fifth International Conference on Hybrid Intelligent Systems
Ensemble confidence estimates posterior probability
MCS'05 Proceedings of the 6th international conference on Multiple Classifier Systems
Switching between selection and fusion in combining classifiers: anexperiment
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Using diversity in classifier set selection for arabic handwritten recognition
MCS'10 Proceedings of the 9th international conference on Multiple Classifier Systems
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There are two main approaches to combine the output of classifiers within a multi-classifier system (MCS), which are: combination-based and selection-based methods. In selection-based methods, only one classifier is needed to correctly classify the input pattern. The choice of a classifier is typically based on the certainty of the current decision. The use of weights can be very useful for the final decision of a selection-based MCS since it can provide a confidence degree for each classifier. This paper presents the use of two confidence measures applied in three selection-based methods. The main aim of this paper is to analyze the benefits of using weights in the main selection-based methods and which confidence measure is more suitable to be used.