# Reliable Multi-label Classification: Prediction with Partial Abstention

## TL;DR

This paper introduces a novel multi-label classification framework allowing partial abstention, formalizes it through loss minimization, and provides initial theoretical and experimental results for various performance metrics.

## Contribution

It extends multi-label classification by enabling partial abstention and formalizes this approach via a generalized loss minimization framework.

## Key findings

- Formalization of multi-label classification with abstention
- Initial theoretical results for Hamming, rank, and F-measure losses
- Experimental validation demonstrating the approach's effectiveness

## Abstract

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.