Rotation-Based Ensembles

A new method for ensemble generation is presented. It is based on grouping the attributes in di erent subgroups, and to apply, for each group, an axis rotation, using Principal Component Analysis. If the used method for the induction of the classi ers is not invariant to rotations in the data set, the generated classi er can be very different. Hence, once of the objectives aimed when generating ensembles is achieved, that the di erent classi ers were rather diverse. The majority of ensemble methods eliminate some information (e.g., instances or attributes) of the data set for obtaining this diversity. The proposed ensemble method transforms the data set in a way such as all the information is preserved. The experimental validation, using decision trees as base classi ers, is favorable to rotation based ensembles when comparing to Bagging, Random Forests and the most well-known version of Boosting.