Classifier ensemble creation via false labelling
This work addresses the challenge of improving classification accuracy in biomedical data analysis, though it appears incremental as it builds on existing ensemble methods.
The paper tackles the problem of classifier ensemble creation by automatically generating optimal labelings for multiple classifiers, which are then applied to original data instances. The approach was evaluated on high-dimensional biomedical datasets and outperformed individual classifiers in all cases.
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.