The GWO-based feature selection approach, while effective, may have certain limitations when dealing with highly correlated or redundant features. Some potential limitations include:
Handling Redundancy: GWO may struggle to effectively handle highly correlated features, leading to the selection of redundant variables. This can impact the interpretability and generalization of the model.
Dimensionality Reduction: In cases where the feature space is high-dimensional, GWO may face challenges in efficiently reducing the dimensionality while maintaining the relevance of selected features.
Optimization Convergence: GWO's convergence behavior may be affected by the presence of redundant features, potentially leading to suboptimal solutions or longer convergence times.
To address these limitations and improve the feature selection process, the GWO-based approach can be further enhanced:
Correlation Analysis: Incorporate correlation analysis techniques to identify and eliminate highly correlated features before applying GWO for feature selection.
Regularization Techniques: Integrate regularization methods within the GWO algorithm to penalize redundant features and promote sparsity in the selected feature subset.
Ensemble Feature Selection: Implement ensemble feature selection strategies that combine multiple feature selection algorithms, including GWO, to enhance the robustness and diversity of the selected features.
By incorporating these improvements, the GWO-based feature selection approach can better handle highly correlated or redundant features, leading to more effective and efficient feature selection outcomes.