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On the Left, The Algorithm 1 details our Multi-Stage Feature Selection. It illustrates our rigorous four-phase approach, where features must survive mutual information filtering, recursive elimination, and model-based ranking. By using a majority voting mechanism where a feature index is only included in the final set, F superscript asterisk, if it appears in at least three out of the four selection sets then we ensure that our input to the deep learning model is highly discriminative and incredibly lean. On the right, we have Algorithm 2, our Hybrid Evolutionary-Spider Wasp Optimization (hEVOSWO). This pseudocode shows how we concurrently optimize both the binary feature mask and the real-valued SVM hyperparameters, C and gamma. It highlights the iterative process where, for each of our 100 iterations, we evaluate the fitness of our population using the F1-score. You can see the Elite solution index mechanism in step 12, which then guides the localized refinement of the other solutions in the search space. This combination of global exploration and local fine-tuning is what allows our model to reach its peak performance. |
IEEE/ICACT20260103 Slide.15
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