International audienceIn ensemble learning field, the voting of different experts can produce an optimal solution. However, the quality of voting depends on the participant expertise. In this paper, an expert selection algorithm is proposed by considering reliability measure extracted from the confidence score. Our method has been applied based on the combination of 6 algorithms. Experimental result using 8 datasets shows that the proposed reliable majority voting algorithm provides a better average accuracy than the ordinary majority voting and the base classi-fiers. keyword: reliable majority voting, classification, ensemble learning
In this paper, a measure of competence based on random classification (MCR) for classifier ensembles...
Majority voting is a popular and robust strategy to aggregate different opinions in learning from cr...
This paper deals with supervised learning for classification. A new general purpose classifier is pr...
International audienceIn ensemble learning field, the voting of different experts can produce an opt...
International audienceIn ensemble learning field, the voting of different experts can produce an opt...
International audienceIn ensemble learning field, the voting of different experts can produce an opt...
International audienceIn ensemble learning field, the voting of different experts can produce an opt...
International audienceIn ensemble learning field, the voting of different experts can produce an opt...
Part 5: Classification - ClusteringInternational audienceThe combination of multiple classifiers can...
Part 5: Classification - ClusteringInternational audienceThe combination of multiple classifiers can...
Abstract. With the increasing volume of data in the world, the best approach for learning from this ...
Ensemble learning combines a series of base classifiers and the final result is assigned to the corr...
Ensemble margin Classification confidence a b s t r a c t Ensemble learning has attracted considerab...
We revisit, from a statistical learning perspective, the classical decision-theoretic problem of wei...
In real world situations every model has some weaknesses and will make errors on training data. Give...
In this paper, a measure of competence based on random classification (MCR) for classifier ensembles...
Majority voting is a popular and robust strategy to aggregate different opinions in learning from cr...
This paper deals with supervised learning for classification. A new general purpose classifier is pr...
International audienceIn ensemble learning field, the voting of different experts can produce an opt...
International audienceIn ensemble learning field, the voting of different experts can produce an opt...
International audienceIn ensemble learning field, the voting of different experts can produce an opt...
International audienceIn ensemble learning field, the voting of different experts can produce an opt...
International audienceIn ensemble learning field, the voting of different experts can produce an opt...
Part 5: Classification - ClusteringInternational audienceThe combination of multiple classifiers can...
Part 5: Classification - ClusteringInternational audienceThe combination of multiple classifiers can...
Abstract. With the increasing volume of data in the world, the best approach for learning from this ...
Ensemble learning combines a series of base classifiers and the final result is assigned to the corr...
Ensemble margin Classification confidence a b s t r a c t Ensemble learning has attracted considerab...
We revisit, from a statistical learning perspective, the classical decision-theoretic problem of wei...
In real world situations every model has some weaknesses and will make errors on training data. Give...
In this paper, a measure of competence based on random classification (MCR) for classifier ensembles...
Majority voting is a popular and robust strategy to aggregate different opinions in learning from cr...
This paper deals with supervised learning for classification. A new general purpose classifier is pr...