Centroid Based Binary Tree Structured SVM for Multi Classification

TL;DR

The paper introduces CBTS-SVM, a binary tree structured approach that reduces classifiers and training/testing time in multi-class SVM, maintaining accuracy and scalability for large datasets.

Contribution

It presents a novel binary tree based SVM algorithm that minimizes classifiers and improves efficiency while maintaining accuracy in multi-class classification.

Findings

CBTS achieves comparable accuracy to OVO.

CBTS outperforms OVA in accuracy with reduced training/testing time.

CBTS is scalable for large datasets.

Abstract

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.