Design of Primal Soft-margin SVM Leveraging Piecewise Linear Approximation based Linear Programming Technique

Abstract

This research introduces a new approach for training soft-margin Support Vector Machines (SVMs) using the primal formulation. The method, called soft-margin Piecewise Linear Approximation based SVM (Soft-margin PLA-SVM), streamlining the optimization of soft-margin SVM hyperparameters in linear programming framework using well-known GUROBI Optimizer Solver. It eliminates the need for an initial hyperparameter guess and uses an adaptable initial search domain. The study uses the Wisconsin Breast Cancer Original dataset from UCI machine learning to validate the effectiveness of proposed soft-margin PLA-SVM. Comparative analysis shows that proposed PLA-SVM outperforms other classifiers in terms of training speed, accuracy, precision, and ROC-AUC scores. The scalability and computational efficiency of soft-margin PLA-SVM make it suitable for high-dimensional and large-scale datasets. The research demonstrates the effectiveness of the primal perspective in solving the soft-margin SVM design problem.