Rotation Forests Training Algorithm
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A Rotation Forests Training Algorithm is an Ensemble Learning Algorithm that is based on Random Forests Training Algorithm for building decision tree ensembles.
- AKA: Rotation Forests.
- Example(s):
- ...
- Counter-Example(s):
- See: Tree Learning Algorithm, Ensemble Learning System, Principal Components Analysis, Decision Tree.
References
2017
- (Sammut & Webb, 2017) ⇒ Claude Sammut, and Geoffrey I. Webb. (2017). "Rotation Forests" In: (Sammut & Webb, 2017) DOI: 10.1007/978-1-4899-7687-1_736
- QUOTE: Rotation Forests is an ensemble learning technique. It is similar to the Random Forests approach to building decision tree ensembles. In the first step, the original feature set is split randomly into a disjoint subsets. Next, principal components analysis is used to extract n principal component dimensions from each of the K subsets. These are then pooled, and the original data projected linearly into this new feature space. A tree is then built from this data in the usual manner. This process is repeated to create an ensemble of trees, each time with a different random split of the original feature set.
As the tree learning algorithm builds the classification regions using hyperplanes parallel to the feature axes, a small rotation of the axes may lead to a very different tree. The effect of rotating the axes is that classification regions of high accuracy can be constructed with far fewer trees than in Bagging and Adaboost.
- QUOTE: Rotation Forests is an ensemble learning technique. It is similar to the Random Forests approach to building decision tree ensembles. In the first step, the original feature set is split randomly into a disjoint subsets. Next, principal components analysis is used to extract n principal component dimensions from each of the K subsets. These are then pooled, and the original data projected linearly into this new feature space. A tree is then built from this data in the usual manner. This process is repeated to create an ensemble of trees, each time with a different random split of the original feature set.