Identifying Suspected Mislabeled Apps in Google Play Application Removal Prediction: An Empirical Comparison of Label Noise Detection Methods
Researchers compare three label-noise detectors on 870,514 Google Play apps used to predict removals.
A cs.CR paper studies label noise in models that predict Google Play app removals, where both voluntary withdrawals and policy takedowns are labeled removed and uncaught spam can remain labeled stable. Isolation Forest, Neighborhood Disagreement, and Prediction Inconsistency were applied to 870,514 apps from a 2022 dataset. Their overlap, 7,598 apps at default settings, is treated as the strongest mislabeling set, but removing flagged apps never beat the baseline and performance worsened as more were removed. A model trained on 3,021 trainable overlap apps reached a test AUC of 0.2518, and VirusTotal and Quark Engine confirmation found flagged apps underrepresented among confirmed removals.
- Store presence labels do not record why an app disappeared.
- Three detectors overlap on 7,598 candidate mislabeled apps.
- Removing flagged apps does not beat the baseline model.
- An overlap-only model scores test AUC 0.2518, below chance.
From the original article
1 min read at arxiv.orgModels that predict which Google Play apps will be removed are trained on labels that record only whether an app was still in the store at a later observation. A disappeared app is labeled removed and a present one stable, but neither records why. A voluntary withdrawal and a policy takedown both produce removed, and an uncaught spam app keeps stable.
By Deborah Dobles Montalvan · arxiv.org
A short excerpt; the article and its images belong to the publisher. Summary, key facts and assessment above are AI-written from the article and can be wrong.