An Experimental Study on Combining Binarization Techniques and Ensemble Methods of Decision Trees

Binarization techniques deal with multiclass classification problem combining several binary classifiers. They were originally introduced for dealing with multiclass problems with methods that were only able to deal with two classes (e.g., SVM). Nevertheless, it has been shown that they can also be useful with classification methods able to deal directly with multiclass problems (e.g., decision trees), because they can improve the results. This work studies if this improvement is also possible when using ensembles of decision trees (e.g., Random Forest, Boosting) over 67 multiclass datasets. It was found that some combinations of a binarization technique and an ensemble method improve the results of the ensemble method without binarization.