The documentation on ensemble methods in Matlab explains different ensemble algorithms for classification and regression tasks. I have normalized the raw feature set and using the normalized data for training and testing.
My data is imbalanced and so I am interested to apply RUSBoost method. It is specifically used for such a scenario. There are some points which are not clear from the documentation and for which I could not find answers in other resources. Few points on ensemble learning cleared from this question on SO
However, I have confusions on applying boosting method with ensemble learning. Here are my questions:
RUSBoostbe applied without using Ensemble classifiers? Are boosting techniques- undersampling and oversampling methods applicable for ensemble classifiers only?
- Why only decision tree can be used with
RUSBoost. I tried using other learners such as
svmtemplatebut these throw errors.
Please correct me where I have misunderstood.