Randomization vs Optimization in SVM Ensembles

摘要

Ensembles of SVMs are notoriously difficult to build because of the stability of the model provided by a single SVM. The application of standard bagging or boosting algorithms generally leads to small accuracy improvements at a computational cost that increases with the size of the ensemble. In this work, we leverage on subsampling and the diversification of hyperparameters through optimization and randomization to build SVM ensembles at a much lower computational cost than training a single SVM on the same data. Furthermore, the accuracy of these ensembles is comparable to a single SVM and to a fully optimized SVM ensemble.