Label Distribution Learning

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 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 LDL algorithms in three ways: problem transformation, algorithm adaptation, and specialized algorithm design. In order to compare their performance, six evaluation measures are suggested for LDL algorithms, and the first batch of real-world label distribution datasets are prepared. Experimental results on the ten real-world datasets show clear advantage of the specialized algorithms, which indicates the importance of special design for the characteristics of the LDL problem.