Evaluation of distance measures for multi-class classification in binary svm decision tree
Date Issued
2010-06-13
Author(s)
Madjarov, Gjorgji
Gjorgjevikj, Dejan
Abstract
Multi-class classification can often be constructed as a generalization of binary classification. The approach that we use for solving
this kind of classification problem is SVM based Binary Decision Tree
architecture (SVM-BDT). It takes advantage of both the efficient computation of the decision tree architecture and the high classification accuracy of SVMs. The hierarchy of binary decision subtasks using SVMs
is designed with a clustering algorithm. In this work, we are investigating how different distance measures for the clustering influence the
predictive performance of the SVM-BDT. The distance measures that
we consider include Euclidian distance, Standardized Euclidean distance
and Mahalanobis distance. We use five different datasets to evaluate the
performance of the SVM based Binary Decision Tree architecture with
different distances. Also, the performance of this architecture is compared with four other SVM based approaches, ensembles of decision trees
and neural network. The results from the experiments suggest that the
performance of the architecture significantly varies depending of applied
distance measure in the clustering process.
this kind of classification problem is SVM based Binary Decision Tree
architecture (SVM-BDT). It takes advantage of both the efficient computation of the decision tree architecture and the high classification accuracy of SVMs. The hierarchy of binary decision subtasks using SVMs
is designed with a clustering algorithm. In this work, we are investigating how different distance measures for the clustering influence the
predictive performance of the SVM-BDT. The distance measures that
we consider include Euclidian distance, Standardized Euclidean distance
and Mahalanobis distance. We use five different datasets to evaluate the
performance of the SVM based Binary Decision Tree architecture with
different distances. Also, the performance of this architecture is compared with four other SVM based approaches, ensembles of decision trees
and neural network. The results from the experiments suggest that the
performance of the architecture significantly varies depending of applied
distance measure in the clustering process.
Subjects
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