The nature of statistical learning theory
The nature of statistical learning theory
Making large-scale support vector machine learning practical
Advances in kernel methods
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Efficient algorithms for decision tree cross-validation
The Journal of Machine Learning Research
An Efficient Three-Stage Classifier for Handwritten Digit Recognition
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 4 - Volume 04
Ensembles of Multi-Objective Decision Trees
ECML '07 Proceedings of the 18th European conference on Machine Learning
Binary tree of SVM: a new fast multiclass training and classification algorithm
IEEE Transactions on Neural Networks
Optimized distance metrics for differential evolution based nearest prototype classifier
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
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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.