<ul><li>A benchmark study was conducted to compare the performance of a Gradient-Optimized Fuzzy Inference System (GF) classifier against various state-of-the-art machine learning models.</li><li>The evaluation was done on five datasets from the UCI Machine Learning Repository, each offering diverse input types, class distributions, and classification complexity.</li><li>Compared to traditional Fuzzy Inference Systems, the GF classifier, which utilizes gradient descent, demonstrated superior classification accuracy, high precision, and significantly lower training times.</li><li>The GF model exhibited consistency across folds and datasets, indicating its robustness in handling noisy data and varying feature sets.</li></ul>

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

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