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cloud.google.com·7h ago
AI Research
arXiv CS.AI·10d ago·5 min read
Partial AUC Maximization from Positive-unlabeled Data
TL;DR
A novel method for maximizing the partial area under the receiver operating characteristic curve (pAUC) using only positive and unlabeled data has been developed. This approach addresses the challenge of lacking labeled negative data, which is often difficult to obtain in real-world applications.
✦ Why It Matters
Implement this method to improve classifier performance in applications where negative data is unavailable.
Key Takeaways
- Utilize positive-unlabeled data for classifier training.
- Apply the derived empirical estimator for pAUC maximization.
- Test the method on diverse real-world datasets.
- Consider implications for fields with limited negative data.
How It Works
The proposed method represents pAUC using only positive and marginal densities, allowing for the estimation of true positive rates within specific false positive rate ranges. By focusing on empirical risk minimization, the method derives a smoothed estimator that can be maximized to train classifiers effectively.