A Comparative Study of Decision Tree Approaches to Multi-Class Support Vector Machines

P. Krauthausen and A. Laubenheimer (Germany)


Pattern Recognition, Decision Tree, and Multi-class SVM.


In this paper we review and evaluate recent decision tree approaches to multi-class SVM for benchmark and self collected image data sets. In addition, we compare the clas sification capabilities of hierarchical agglomerative and hi erarchical divisive clustering approaches which recursively partition the set of classes with the standard pair wise clas sifier. We compare agglomerative clustering approaches based on the pair wise Euclidean distance of class means, pair wise misclassification rates for a binary SVM and a Mahalanobis-assignment as well as divisive clustering us ing k-Means to partition a set of classes based on a partition of the data or one-class-ν-SVM class representatives. Our results show that decision tree approaches achieve classifi cation performance similar to the default multi-class SVM.

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