A stochastic configuration network based on an improved sampling strategy and Bayesian optimization

Stochastic configuration network (SCN) is an incremental random learning model that achieves fast convergence through a supervised mechanism, making it well-suited for adapting to variations in data characteristics across different regression and classification tasks. However, due to the inherent limitations of fully connected neural networks, the original random sampling strategy may lead to a decline in SCN’s generalization performance. Bayesian optimization, a global optimization method based on Bayesian statistics and Gaussian processes, can efficiently and accurately identify the optimal hyperparameters for model performance. Based on this, this paper proposes a Bayesian optimization-based stochastic configuration network (BO-SCN) algorithm, which integrates an improved sampling strategy and Bayesian optimization. First, a scaling factor $s$ is introduced as a new hyperparameter into both uniform and normal distribution sampling strategies to ensure that the sampled weight values remain relatively small. Second, Bayesian optimization is employed to automatically select the optimal value of $s$, and an optimal search range for $s$ is proposed, minimizing manual intervention while maximizing model performance. Finally, the performance of BO-SCN is evaluated on ten benchmark datasets. Experimental results demonstrate that the proposed algorithm not only enhances prediction accuracy and maintains stability but also significantly reduces the complexity of hyperparameter tuning.