Data mining can be considerably improved with the inclusion of prior domain knowledge; such knowledge reveals complex patterns that might otherwise remain hidden. Among such patterns, monotonic relationships between variables are crucial because of their applicability in real-world contexts. Although considerable growth has occurred in the development of monotonic classification models, many of these models excel in binary or multiclass classification but falter in one-class classification. To address this problem, we developed a monotonicity-constrained support vector domain description (MC-SVDD) model in this study. This model is an innovative evolution of the monotonicity-constrained support vector machine model and is specifically designed for one-class classification with strict adherence to monotonicity constraints. In the developed MC-SVDD model, monotonicity constraints are integrated into the well-established support vector domain description (SVDD) framework. Moreover, methods such as quadratic programming and data visualization are incorporated into the MC-SVDD model. In extensive evaluations, the MC-SVDD model outperformed a conventional SVDD model in prediction performance. This study makes a key contribution to domain-driven data mining.

Monotonic classification; One-class classification; Monotonicity-constrained SVM; Support vector data description (SVDD); domain-driven data mining