Abstract

A Hierarchical Method for Multi-Class Support Vector Machines
Volkan Vural - Northeastern University Jennifer Dy - Northeastern University
We introduce a framework, which we call Divide-by-2 (DB2), for extending support vector machines (SVM) to multi-class problems. DB2 offers an alternative to the standard one-against-one and one-against-rest algorithms. For an $N$ class problem, DB2 produces an $N-1$ node binary decision tree where nodes represent decision boundaries formed by $N-1$ SVM binary classifiers. This tree structure allows us to present a generalization and a time complexity analysis of DB2. Our analysis and related experiments show that, DB2 is faster than one-against-one and one-against-rest algorithms in terms of testing time, significantly faster than one-against-rest in terms of training time, and that the cross-validation accuracy of DB2 is comparable to these two methods.

A Hierarchical Method for Multi-Class Support Vector Machines

Volkan Vural - Northeastern University
Jennifer Dy - Northeastern University

We introduce a framework, which we call Divide-by-2 (DB2), for extending support vector machines (SVM) to multi-class problems. DB2 offers an alternative to the standard one-against-one and one-against-rest algorithms. For an $N$ class problem, DB2 produces an $N-1$ node binary decision tree where nodes represent decision boundaries formed by $N-1$ SVM binary classifiers. This tree structure allows us to present a generalization and a time complexity analysis of DB2. Our analysis and related experiments show that, DB2 is faster than one-against-one and one-against-rest algorithms in terms of testing time, significantly faster than one-against-rest in terms of training time, and that the cross-validation accuracy of DB2 is comparable to these two methods.