Fusing Monotonic and One-Class Classification: Elevating SVM with the MC-SVDD Strategy
Year: 2025
Type: article
Abstract: 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.
DOI: 10.3897/jucs.135070
Authors: Ming-Lung Hsu, Yu-Wei Liu, Sheng Tun Li
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W7083193953
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