Learning from Imbalanced Data¶
Haibo He, & Garcia, E. A. (2009). Learning from Imbalanced Data. IEEE Transactions on Knowledge and Data Engineering, 21(9), 1263-1284.
Cited by¶
2 citations across 2 artifacts.
Each citation links to the sentence it supports in the citing article.
Primes¶
- False Positive Paradox
- In machine learning at scale, class imbalance is the paradox in algorithmic dress, and practitioners reach for precision, recall, and PR-AUC precisely to surface it.
This sourceSurveys class-imbalance methods — precision/recall, PR curves, cost-sensitive learning, threshold tuning — that surface and address the rare-positive regime, supporting the machine-learning use and the ML-to-ecology toolbox transfer.
- In machine learning at scale, class imbalance is the paradox in algorithmic dress, and practitioners reach for precision, recall, and PR-AUC precisely to surface it.
- Majority-Dominated Aggregate Objective
- Machine learning — aggregate cross-entropy on an imbalanced dataset is dominated by majority-class terms; the classifier learns "predict majority" as a low-loss strategy and fails on the minority the application cares about (diagnosis, fraud, rare-defect detection).
This sourceSurveys class imbalance: an aggregate loss dominated by the majority class makes 'predict majority' a low-loss strategy with near-zero minority recall, with reweighting, resampling, and threshold-tuning as remedies.
- Machine learning — aggregate cross-entropy on an imbalanced dataset is dominated by majority-class terms; the classifier learns "predict majority" as a low-loss strategy and fails on the minority the application cares about (diagnosis, fraud, rare-defect detection).
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