Rotation-based ensembles of RBF networks

Abstract. Ensemble methods allow to improve the accuracy of classification methods. This work considers the application of one of these methods, named Rotation-based, when the classifiers to combine are RBF Networks. This ensemble method, for each member of the ensemble, transforms the data set using a pseudo-random rotation of the axis. Then the classifier is constructed using this rotation data. The results of the ensembles obtained with this method are compared with the results using other ensemble methods (including Bagging and Boosting), over 34 data sets.

• Type: Conference
• Year: 2006
• Where: ESANN
• Authors: Juan José Rodríguez, Jesús Maudes, Carlos J. Alonso
• Keywords: Classification Methods, Ensemble Methods, ESANN 2006, ESANN 2007, Rbf Networks