arXiv Machine Learning September 25, 2026 By Md Rafid Islam, Zahid Hasan, Hafiz Abdur Rahman

Classifier-Dependent Benefits of Pseudo-Labeling for Semi-Supervised Android Malware Attribution

Read the original on arXiv Machine Learning →

The study evaluates pseudo‑labeling for semi‑supervised learning on Android malware attribution using six classifiers. Results show that the benefit of SSL varies strongly by classifier: SVM gains the most, LightGBM improves modestly, and Random Forest can be harmed at low label ratios. The approach particularly helps hard‑to‑classify families and achieves near‑optimal performance with about 800 labeled samples.

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