Table 4.

Summary of comparison

Metric Proposed Cso-Xg-Boost Baseline Xg-Boost Other Ids solutions with tuning
Performance High accuracy, precision, F1-score Moderate without tuning Varies, often lower or comparable
Optimization Efficiency Efficient, balances exploration/exploitation No optimization, less efficient Slower (Grid Search) or less thorough (Random Search)
Generalization Robust, reduces overfitting Moderate, possible overfitting Depends on model, usually lower
Handling Imbalanced Data Excellent, optimized parameters Moderate, default parameters Depends on model, often requires resampling
Computational Complexity Low to moderate, scalable Efficient but not optimized Varies, typically higher than XG-Boost
Flexibility High, adaptable to various tasks Limited without tuning Moderate, depends on the solution

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