Advancing SVM classification: Parallelizing conjugate gradient for monotonicity enforcement

With the advent of multimedia, social media, and the Internet of Things, an unprecedented volume of data is being generated at a remarkable speed. Therefore, the application of data mining techniques has become essential for solving large-scale and increasingly complex problems. The integration of prior knowledge into data mining has also become a trending and challenging concern. This study proposed a novel support vector machine (SVM) model designed to address this concern. The model incorporates expert knowledge regarding the monotonic relations between response and predictor variables, represented through monotonicity constraints. In our approach, monotonic constraint SVMs were formulated by integrating regularization, monotonicity constraints, a box-constrained conjugate gradient, and a parallel strategy into a model to ensure solution uniqueness and boundedness. The model's ability to retain monotonicity was assessed using the frequency monotonicity rate. The experimental results highlight the feasibility and effectiveness of the proposed model, PBCCG-RMC-SVM, in addressing classification problems with monotonic prior knowledge. Additionally, the adoption of a parallel strategy accelerates the generation of analytical or prediction results, and therefore, the model can enable managers to make faster and more accurate decisions through data analysis.