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Classifier ensemble creation via false labelling
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DOI：10.1016/j.knosys.2015.07.009.png)
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Abstract
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In this paper, a novel approach to classifier ensemble creation is presented. While other ensemble creation techniques are based on careful selection of existing classifiers or preprocessing of the data, the presented approach automatically creates an optimal labelling for a number of classifiers, which are then assigned to the original data instances and fed to classifiers. The approach has been evaluated on high-dimensional biomedical datasets. The results show that the approach outperformed individual approaches in all cases. (C) 2015 Elsevier B.V. All rights reserved.
Keywords:
Ensemble learning
Diversity
Hidden Markov Random Fields
Simulated annealing
Bioinformatics
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Attribute bagging: improving accuracy of classifier ensembles by using random feature subsets.png)
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