Label Distribution Learning

TL;DR

This paper introduces label distribution learning (LDL), a new framework that captures the importance of multiple labels per instance, generalizing multi-label learning, with algorithms and datasets demonstrating its effectiveness.

Contribution

It proposes the LDL paradigm, develops six algorithms for it, and provides datasets and evaluation measures, advancing multi-label learning to handle label importance distributions.

Findings

Specialized LDL algorithms outperform general methods.

LDL effectively models label importance in real-world data.

New datasets and measures facilitate LDL research.

Abstract

Although multi-label learning can deal with many problems with label ambiguity, it does not fit some real applications well where the overall distribution of the importance of the labels matters. This paper proposes a novel learning paradigm named \emph{label distribution learning} (LDL) for such kind of applications. The label distribution covers a certain number of labels, representing the degree to which each label describes the instance. LDL is a more general learning framework which includes both single-label and multi-label learning as its special cases. This paper proposes six working LDL algorithms in three ways: problem transformation, algorithm adaptation, and specialized algorithm design. In order to compare the performance of the LDL algorithms, six representative and diverse evaluation measures are selected via a clustering analysis, and the first batch of label distribution datasets are collected and made publicly available. Experimental results on one artificial and fifteen real-world datasets show clear advantages of the specialized algorithms, which indicates the importance of special design for the characteristics of the LDL problem.