Gradient-Optimized Fuzzy Classifier: A Benchmark Study Against State-of-the-Art Models

• URL: http://arxiv.org/abs/2504.16263v1
• Date: Tue, 22 Apr 2025 20:47:06 GMT
• Title: Gradient-Optimized Fuzzy Classifier: A Benchmark Study Against State-of-the-Art Models
• Authors: Magnus Sieverding, Nathan Steffen, Kelly Cohen,
• Abstract summary: This paper presents a performance benchmarking study of a Gradient-d Fuzzy Inference System (GF) against several state-of-the-art machine learning models.<n>Results demonstrate that the GF model achieved competitive, and in several cases superior, classification accuracy while maintaining high precision and exceptionally low training times.<n>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.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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