Abstract
Feature selection plays an important role in medical classification, particularly when datasets contain high-dimensional and redundant information. At the same time, the performance of a Random Weight Network (RWN) depends on its configuration, making the joint optimization of feature subsets and network parameters a relevant problem. This study proposes a metaheuristic-based framework that integrates feature selection with RWN configuration optimization. The framework simultaneously determines the selected features, active hidden neurons, regularization parameter, and activation function. Eight binary transfer functions, including four S-shaped and four V-shaped functions, are investigated to convert continuous search variables into binary decisions for feature and hidden-neuron selection. The framework is evaluated using four metaheuristic algorithms: Grey Wolf Optimizer (GWO), Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Differential Evolution (DE). Experiments are conducted on 20 benchmark medical datasets under consistent experimental conditions. The results show that the S2 transfer function provides the most favorable overall performance among the investigated transfer functions. Using S2, GWO achieves the best overall Friedman ranking among the four optimizers, although the overall differences are not statistically significant at the 5% level for some evaluation measures. The resulting GWO-S2-RWN configuration also demonstrates competitive performance when compared with established feature-selection and classification methods reported in the literature. Overall, the findings show that jointly optimizing feature selection and RWN configuration provides a flexible approach for balancing classification performance and model complexity in medical classification tasks.