arXiv Machine Learning
Hierarchical Utility Calibration for Structured Multiclass Decisions
Read the original on arXiv Machine Learning →The Flow has not summarised this story yet — read it at arXiv Machine Learning.
Related stories
arXiv Computation and Language
Sep 23
How to Estimate Whether You Have Found Several Needles in a Haystack: Measuring Calibration in Multi-Label Text Classification
arXiv:2609.26468v1 Announce Type: new Abstract: A key factor in deciding whether to trust an automatic prediction is its confidence score, which should be calibrated to match the actual probability o...
Hugging Face Trending Papers
Sep 22
How to Estimate Whether You Have Found Several Needles in a Haystack: Measuring Calibration in Multi-Label Text Classification
A key factor in deciding whether to trust an automatic prediction is its confidence score, which should be calibrated to match the actual probability of the prediction being correct. Most confidence c...
arXiv Machine Learning
Jul 8
Taxlifier: Leveraging Disease Taxonomy for Enhanced Multi-Label Classification in Chest Radiography
arXiv:2607. 05628v1 Announce Type: cross Abstract: Accurate and efficient classification of thoracic diseases in chest X-ray (CXR) images is crucial for timely diagnosis and treatment.
arXiv Machine Learning
Jul 8
Label Hierarchy Transition: Delving into Class Hierarchies to Enhance Deep Classifiers
arXiv:2112. 02353v3 Announce Type: replace-cross Abstract: Hierarchical classification aims to sort the object into a hierarchical structure of categories.
arXiv Machine Learning
Jun 3
Hierarchies of Calibration: Classification meets Regression
arXiv:2606. 03245v1 Announce Type: cross Abstract: Concepts of calibration formalize the compatibility between probabilistic predictions and the respective outcomes.
arXiv AI
Jun 11
Improving Detection of Rare Nodes in Hierarchical Multi-Label Learning
arXiv:2602. 08986v2 Announce Type: replace-cross Abstract: In hierarchical multi-label classification, a persistent challenge is enabling model predictions to reach deeper levels of the hierarchy for more detailed or fine-grained classifications.