Abstract
This paper studies sinusoidal modulation in rough inclusion classifiers. Attribute contributions are controlled by three interpretable parameters: amplitude, period, and vertical shift. The revised formulation normalizes the sinusoidal phase with respect to the number of attributes, making the period parameter directly interpretable. Experiments on five benchmark datasets with MCCV-10 validation show that random sinusoidal perturbations usually preserve baseline behavior, while targeted tuning improves balanced accuracy.
Paper Type
Poster
DOI
10.62036/ISD.2026.84
On the Dual Nature of Sinusoidal Rough Inclusion Classifiers: From Structural Immunity to Parametric Optimization
This paper studies sinusoidal modulation in rough inclusion classifiers. Attribute contributions are controlled by three interpretable parameters: amplitude, period, and vertical shift. The revised formulation normalizes the sinusoidal phase with respect to the number of attributes, making the period parameter directly interpretable. Experiments on five benchmark datasets with MCCV-10 validation show that random sinusoidal perturbations usually preserve baseline behavior, while targeted tuning improves balanced accuracy.
Recommended Citation
Samojluk, A. & Artiemjew, P.(2026). On the Dual Nature of Sinusoidal Rough Inclusion Classifiers: From Structural Immunity to Parametric Optimization. In M. Valenta, B. Mannová, R. Pergl, A. Przybylek, M. Lang, H. Linger, C. Schneider, N. Iivari, & E. Insfran (Eds.), Making ISD Sustainable: Reloaded with AI and Automation (ISD2026 Proceedings). Prague, Czech Republic: Czech Technical University in Prague. ISBN: 978-80-01-07585-2. https://doi.org/10.62036/ISD.2026.84