# Reliable Multi-label Classification: Prediction with Partial Abstention

arXiv: https://arxiv.org/abs/1904.09235 · HTML: https://mlsanity.com/paper/1904.09235/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Apr 2019.

## Built on by (most cited first, 6 of 6)

- [Machine Learning with a Reject Option: A survey](https://mlsanity.com/paper/2107.11277/) (2021)
- [Unsupervised Anomaly Detection with Rejection](https://mlsanity.com/paper/2305.13189/) (2023)
- [Skeptical binary inferences in multi-label problems with sets of probabilities](https://mlsanity.com/paper/2205.00662/) (2022)
- [Multi-label Chaining with Imprecise Probabilities](https://mlsanity.com/paper/2107.07443/) (2021)
- [Uncertainty Quantification and Resource-Demanding Computer Vision Applications of Deep Learning](https://mlsanity.com/paper/2205.14917/) (2022)
- [Estimating Multi-label Accuracy using Labelset Distributions](https://mlsanity.com/paper/2209.04163/) (2022)

## Abstract (arXiv)

In contrast to conventional (single-label) classification, the setting of multilabel classification (MLC) allows an instance to belong to several classes simultaneously. Thus, instead of selecting a single class label, predictions take the form of a subset of all labels. In this paper, we study an extension of the setting of MLC, in which the learner is allowed to partially abstain from a prediction, that is, to deliver predictions on some but not necessarily all class labels. We propose a formalization of MLC with abstention in terms of a generalized loss minimization problem and present first results for the case of the Hamming loss, rank loss, and F-measure, both theoretical and experimental.