Data Enhancement for Binary Classification of Relational Data
Summary: Data enhancement for relational data classifiers to guard against adversarial poisoning in training data and unseen prediction-time attacks. A training-and-data-enhancement framework with algorithms to detect corrupted features and curate adversarial examples, achieving 20.4% robustness gains and 2.02x speed over SOTA without accuracy loss. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Wenfei Fan
- 2. Xiaoyu Han
- 3. Weilong Ren
- 4. Zihuan Xu
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