Ensembles of binary svm decision trees
Journal
ICT Innovations 2010 Web Proceedings ISSN
Date Issued
2010
Author(s)
Madjarov, Gjorgji
Gjorgjevikj, Dejan
Abstract
Ensemble methods are able to improve the predictive performance of
many base classifiers. In this paper, we consider two ensemble learning
techniques, bagging and random forests, and apply them to Binary SVM
Decision Tree (SVM-BDT). Binary SVM Decision Tree is a tree based
architecture that utilizes support vector machines for solving multiclass
problems. It takes advantage of both the efficient computation of the decision
tree architecture and the high classification accuracy of SVMs. In this paper we
empirically investigate the performance of ensembles of SVM-BDTs. Our most
important conclusions are: (1) ensembles of SVM-BDTs yield noticeable better
predictive performance than the base classifier (SVM-BDT), and (2) the
random forests ensemble technique is more suitable than bagging for SVMBDT.
many base classifiers. In this paper, we consider two ensemble learning
techniques, bagging and random forests, and apply them to Binary SVM
Decision Tree (SVM-BDT). Binary SVM Decision Tree is a tree based
architecture that utilizes support vector machines for solving multiclass
problems. It takes advantage of both the efficient computation of the decision
tree architecture and the high classification accuracy of SVMs. In this paper we
empirically investigate the performance of ensembles of SVM-BDTs. Our most
important conclusions are: (1) ensembles of SVM-BDTs yield noticeable better
predictive performance than the base classifier (SVM-BDT), and (2) the
random forests ensemble technique is more suitable than bagging for SVMBDT.
Subjects
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