Binary Classification Using (Nonlinear) Twin Support Vector Machines

We can employ (Nonlinear) Twin Support Vector Machines (TSVMs) to solve various binary classification problems. TSVM is a powerful algorithm that effectively handles data with complex features and nonlinear relationships. The core implementation involves solving two smaller quadratic programming problems instead of one large optimization problem, making it computationally more efficient than standard SVMs. Through kernel tricks (e.g., RBF or polynomial kernels), TSVM learns to map input data into a high-dimensional feature space where nonlinear patterns become linearly separable. The algorithm operates by generating two non-parallel hyperplanes where each hyperplane is closer to one class while being as far as possible from the other class. Key functions typically involve formulating twin quadratic optimization problems with constraints, implementing kernel matrix calculations, and solving dual problems using sequential minimal optimization (SMO) techniques. By analyzing training data, TSVM determines optimal decision boundaries that maximize margin separation between classes, enabling accurate classification and prediction. Thus, leveraging Twin Support Vector Machines provides an efficient approach for resolving complex binary classification challenges with enhanced computational performance.