C4.5: programs for machine learning
C4.5: programs for machine learning
A Method of Combining Multiple Experts for the Recognition of Unconstrained Handwritten Numerals
IEEE Transactions on Pattern Analysis and Machine Intelligence
Combination of Multiple Classifiers Using Local Accuracy Estimates
IEEE Transactions on Pattern Analysis and Machine Intelligence
Adaptive Selection of Image Classifiers
ICIAP '97 Proceedings of the 9th International Conference on Image Analysis and Processing-Volume I - Volume I
Methods for Dynamic Classifier Selection
ICIAP '99 Proceedings of the 10th International Conference on Image Analysis and Processing
MCS '00 Proceedings of the First International Workshop on Multiple Classifier Systems
Evolution of Multi-class Single Layer Perceptron
ICANNGA '07 Proceedings of the 8th international conference on Adaptive and Natural Computing Algorithms, Part II
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In the field of pattern recognition, the concept of Multiple Classifier Systems (MCSs) was proposed as a method for the development of high performance classification systems. At present, the common "operation" mechanism of MCSs is the "combination" of classifiers outputs. Recently, some researchers pointed out the potentialities of "dynamic classifier selection" (DCS) as a new operation mechanism. In this paper, a DCS algorithm based on the MCS behaviour is presented. The proposed method is aimed to exploit the behaviour of the MCS in order to select, for each test pattern, the classifier that is more likely to provide the correct classification. Reported results on the classification of different data sets show that dynamic classifier selection based on MCS behaviour is an effective operation mechanism for MCSs.