<p>Imbalanced data classification is one of the most critical challenges in machine learning. Standard classifiers do not provide adequate accuracy for detecting minority samples because these classifiers are biased toward the majority samples. To overcome this drawback, many methods have been proposed. One prominent method is the Gravitational Fixed Radius Nearest Neighbor (GFRNN) algorithm, which applies Newton’s law of universal gravitation to determine the class of a test sample based on two parameters: mass and radius. Although GFRNN shows good performance on some imbalanced datasets, it faces several fundamental problems, including ignoring the data distribution and the improper calculation of radius and mass. In this study, a Gravitational Dynamic Radius Nearest Neighbor trained by a Fuzzy Enhanced Hiking Optimization Algorithm (FEHOA-GDRNN) is proposed to improve GFRNN performance. In FEHOA-GDRNN, Enhanced Hiking Optimization Algorithm (EHOA) applies a new spider web search to find better solution based on a Mamdani Fuzzy Inference System (FIS). FEHOA-GDRNN is evaluated on 40 imbalanced datasets and its results are compared with GDRNN trained by Fuzzy HOA (FHOA-GDRNN), GFRNN trained by HOA (HOA-GFRNN), GFRNN and its various versions (IGFRNN, I-GFRNN, and EGDRNN), Cost-Sensitive Support Vector Machine with an RBF kernel (CS-SVM-RBF), Cost-Sensitive Support Vector Machine with a Linear kernel (CS-SVM-Linear), Cost-Sensitive Naïve Bayes (CS-NB), Binary Decision tree (BDT), Random Forest (RF), Gaussian-Probabilistic Neural Network (GaussianPNN) and Skew-Probabilistic Neural Network (SkewPNN). Moreover, the results of the proposed classifier are compared with those of several Fuzzy K-Nearest Neighbor (FKNN). The results demonstrate that FEHOA-GDRNN outperforms other methods in key metrics, including Average Accuracy (AAcc) and Geometric Mean (GM).</p>