AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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This study adapts TabKANet to the all-numerical, highly imbalanced SDP setting and empirically evaluates it against established baselines, using a structured ablation in order to isolate the contribution of oversampling and feature selection rather than to propose a new architecture.
TabKANet is a competitive architecture for all-numerical, highly imbalanced SDP, matching strong neural baselines and surpassing TabNet, where effective class weighting alone suffices and SMOTE is counter-productive.
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