A Comparative Study of Popular Multiclass SVM Classification Techniques and Improvement over Directed Acyclic Graph SVM - Saha, Sanjib (International Journal of Computer Sciences and Engineering, 2026)
Multiclass classification using Support Vector Machine (SVM) is an ongoing research issue. SVM is mainly a binary classifier, but for classification efficiency, it is also used for multiclass classification. In multiclass classification, there are two or more classes and classification is not so easy. That’s why many methods are introduced to extend the classification efficiency of SVM. Directed Acyclic Graph Support Vector Machine (DAGSVM), Binary Tree of Support Vector Machine (BTS) and Error Correcting Output Codes (ECOC) methods are more favourable because of their computation efficiency. In the case of DAGSVM there are many improved methods like Decision Directed Acyclic Graph (DDAG), Divide-by-2 (DB2), and Weighted Directed Acyclic Graph of Support Vector Machine (WDAG SVM) have been developed. The BTS-based methods are SVM with Binary Tree Architecture, and Adaptive Binary Tree (ABT). There are many methods related to ECOC like One- Per-Class (OPC), Discriminant Error Correcting Output Codes (DECOC), and Adaptive ECOC. This paper presented a comparative and analytical survey of those methods and introduces a new model which is an improvement over the existing DAGSVM methods. This model uses Gaussian Mixture Model, K-Means Clustering and Naïve Bayes Classifier for data classification. This model can give better results than existing DAGSVM method