  Support Vector Machines (SVMs) were primarily designed for 2-class
classification. But they have been extended for N-class classification also
based on the requirement of multiclasses in the practical applications.
Although N-class classification using SVM has considerable research attention,
getting minimum number of classifiers at the time of training and testing is
still a continuing research. We propose a new algorithm CBTS-SVM (Centroid
based Binary Tree Structured SVM) which addresses this issue. In this we build
a binary tree of SVM models based on the similarity of the class labels by
finding their distance from the corresponding centroids at the root level. The
experimental results demonstrates the comparable accuracy for CBTS with OVO
with reasonable gamma and cost values. On the other hand when CBTS is compared
with OVA, it gives the better accuracy with reduced training time and testing
time. Furthermore CBTS is also scalable as it is able to handle the large data
sets.
