This paper presents a performance benchmarking study of a Gradient-Optimized
Fuzzy Inference System (GF) classifier against several state-of-the-art machine
learning models, including Random Forest, XGBoost, Logistic Regression, Support
Vector Machines, and Neural Networks. The evaluation was conducted across five
datasets from the UCI Machine Learning Repository, each chosen for their
diversity in input types, class distributions, and classification complexity.
Unlike traditional Fuzzy Inference Systems that rely on derivative-free
optimization methods, the GF leverages gradient descent to significantly
improving training efficiency and predictive performance. Results demonstrate
that the GF model achieved competitive, and in several cases superior,
classification accuracy while maintaining high precision and exceptionally low
training times. In particular, the GF exhibited strong consistency across folds
and datasets, underscoring its robustness in handling noisy data and variable
feature sets. These findings support the potential of gradient optimized fuzzy
systems as interpretable, efficient, and adaptable alternatives to more complex
deep learning models in supervised learning tasks.

Dieser Artikel untersucht Zeitreisen und deren Auswirkungen.

PDF herunterladen: