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TabKANet for Software Defect Prediction: An Oversampling and Feature-Selection Ablation Study

Setyo Wahyu Saputro M. Faza Azhiman Saputra Setyo W. Saputro M. Faisal Radityo Adi Nugroho Andi Farmadi
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TL;DR

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.

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