A Multicategory Classification Model with Regularized Pairwise Comparison - Yeo, Donghwa (International Journal of Industrial Engineering: Theory, Applications and Practice, 2026)
Multicategory classification poses a significant challenge in machine learning because real-world problems often involve multiple classes, while many algorithms are tailored for binary classification. Among the methods for extending binary classifiers to multicategory classification problems, two representative approaches are the “One-versus-The-Rest” (OVT) and “One-versus-One” (OVO) methods. We demonstrate that the OVT method can encounter masking issues in certain situations and propose a new algorithm using the OVO approach to overcome this problem. In particular, we develop a method that integrates the OVO approach with the Mallows-Bradley-Terry model to estimate pairwise probabilities and incorporates regularization techniques into a single optimization framework to reduce computational cost. The penalty functions considered include LASSO and Ridge-LASSO penalties. Experimental results and real data analysis indicate that the proposed method outperforms the OVT method and is less affected by the masking problem.