ML-CLAPPS: A Probabilistic Framework for Imbalanced Multilabel Learning under Class Prioritization
We introduce the Multilabel Class-Prioritized Probabilistic Sampler (ML-CLAPPS), a learnable sampler that optimizes a distribution over training instances to control the model's exposure to different classes.
ProofPaper ↗
Key points
- Real-world datasets often exhibit severe class imbalance, degrading the performance of deep learning models, particularly on rare classes.
- This approach addresses multilabel imbalance while enabling prioritization of significant conditions via a user-defined importance vector (IV).
- Empirical evaluation on a real-world multilabel ECG image dataset demonstrates that ML-CLAPPS achieves state-of-the-art performance.
- We show that it aligns class exposure with the specified IV, and that increasing a class's importance leads to corresponding improvements in its classification performance, enabling explicit prioritization of clinically critical conditions.
Sources (1)
- [1]ML-CLAPPS: A Probabilistic Framework for Imbalanced Multilabel Learning under Class PrioritizationarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 11, 06:15 PM
We introduce the Multilabel Class-Prioritized Probabilistic Sampler (ML-CLAPPS), a learnable sampler that optimizes a distribution over training instances to control the model's exposure to different classes.
Real-world datasets often exhibit severe class imbalance, degrading the performance of deep learning models, particularly on rare classes.
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