梯度优化模糊分类器在多个数据集上表现优异，训练快且准确。
Gradient-Optimized Fuzzy Classifier: A Benchmark Study Against State-of-the-Art Models
- 用梯度下降优化模糊系统，提升训练效率和性能。
- 在5个数据集上准确率媲美甚至超过主流模型，训练时间极短。
- 适合需要可解释性与高效推理的工业场景应用。
本文对梯度优化模糊推理系统（GF）分类器与多种前沿机器学习模型（包括随机森林、XGBoost、逻辑回归、支持向量机和神经网络）进行了性能基准测试。实验基于来自UCI机器学习仓库的五个数据集，涵盖不同输入类型、类别分布和分类复杂度。与依赖无导数优化的传统模糊系统不同，GF采用梯度下降方法，显著提升了训练效率和预测性能。结果表明，GF在多个数据集上实现了具有竞争力甚至更优的分类准确率，同时保持高精度和极低的训练时间。尤其在不同数据划分中表现出高度一致性，验证了其在噪声数据和异构特征集下的鲁棒性。这些发现支持梯度优化模糊系统作为可解释、高效且适应性强的监督学习替代方案，在复杂深度学习模型之外具备应用潜力。
原文摘要 · Abstract (English)
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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