A Unified View of Multi-Label Performance Measures

TL;DR

This paper introduces a unified margin-based framework to understand and optimize multiple performance measures in multi-label classification, providing theoretical insights and a practical max-margin algorithm called LIMO.

Contribution

It proposes a unified margin view for eleven multi-label performance measures and develops a max-margin method, LIMO, to optimize them effectively.

Findings

LIMO outperforms existing methods on several benchmarks.

The margin framework explains the performance differences across measures.

Theoretical proofs connect margins to specific performance measures.

Abstract

Multi-label classification deals with the problem where each instance is associated with multiple class labels. Because evaluation in multi-label classification is more complicated than single-label setting, a number of performance measures have been proposed. It is noticed that an algorithm usually performs differently on different measures. Therefore, it is important to understand which algorithms perform well on which measure(s) and why. In this paper, we propose a unified margin view to revisit eleven performance measures in multi-label classification. In particular, we define label-wise margin and instance-wise margin, and prove that through maximizing these margins, different corresponding performance measures will be optimized. Based on the defined margins, a max-margin approach called LIMO is designed and empirical results verify our theoretical findings.