A probabilistic model of classifier competence for dynamic ensemble selection
Pattern Recognition
HAIS'11 Proceedings of the 6th international conference on Hybrid artificial intelligent systems - Volume Part II
ITIB'12 Proceedings of the Third international conference on Information Technologies in Biomedicine
New dynamic classifiers selection approach for handwritten recognition
ICANN'12 Proceedings of the 22nd international conference on Artificial Neural Networks and Machine Learning - Volume Part II
Expert Systems with Applications: An International Journal
International Journal of Knowledge-based and Intelligent Engineering Systems
Hi-index | 0.00 |
This paper presents a measure of competence based on a randomized reference classifier (RRC) for classifier ensembles. The RRC can be used to model, in terms of class supports, any classifier in the ensemble. The competence of a modelled classifier is calculated as the probability of correct classification of the respective RRC. A multiple classifier system (MCS) was developed and its performance was compared against five MCSs using eight databases taken from the UCI Machine Learning Repository. The system developed achieved the highest overall classification accuracies for both homogeneous and heterogeneous ensembles.