Rotation Forest: A New Classifier Ensemble Method
@article{Diez2006RotationFA, title={Rotation Forest: A New Classifier Ensemble Method}, author={Juan Jos{\'e} Rodr{\'i}guez Diez and L. Kuncheva and C. Alonso}, journal={IEEE Transactions on Pattern Analysis and Machine Intelligence}, year={2006}, volume={28}, pages={1619-1630} }
We propose a method for generating classifier ensembles based on feature extraction. [...] Key Method To create the training data for a base classifier, the feature set is randomly split into K subsets (K is a parameter of the algorithm) and principal component analysis (PCA) is applied to each subset. All principal components are retained in order to preserve the variability information in the data. Thus, K axis rotations take place to form the new features for a base classifier. The idea of the rotation…Expand
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