Comments on "a parallel mixture of SVMs for very large scale problems".
Collobert, Bengio, and Bengio (2002) recently introduced a novel approach to using a neural network to provide a class prediction from an ensemble of support vector machines (SVMs). This approach has the advantage that the required computation scales well to very large data sets. Experiments on the Forest Cover data set show that this parallel mixture is more accurate than a single SVM, with 90.72% accuracy reported on an independent test set. Although this accuracy is impressive, their article does not consider alternative types of classifiers. We show that a simple ensemble of decision trees results in a higher accuracy, 94.75%, and is computationally efficient. This result is somewhat surprising and illustrates the general value of experimental comparisons using different types of classifiers.
Liu
Xiaomei
X
Department of Computer Science and Engineering, University of Notre Dame, South Bend, IN 46556, USA. xliu5@cse.nd.edu
Hall
Lawrence O
LO
Bowyer
Kevin W
KW
D000465:Algorithms; D001185:Artificial Intelligence; D016571:Neural Networks, Computer; D012984:Software