Label Enhancement for Label Distribution Learning

Multi-label learning (MLL) deals with the case where each instance is associated with multiple labels. Its target is to learn the mapping from instance to relevant label set. Most existing MLL methods adopt the uniform label distribution assumption, i.e., the importance of all relevant (positive) labels is the same for the instance. However, for many real-world learning problems, the importance of different relevant labels is often different. For this issue, label distribution learning (LDL) has achieved good results by modeling the different importance of labels with a label distribution. Unfortunately, many datasets only contain simple logical labels rather than label distributions. To solve the problem, one way is to transform the logical labels into label distributions by mining the hidden label importance from the training examples, and then promote prediction precision via label distribution learning. Such process of transforming logical labels into label distributions is defined as label enhancement for label distribution learning. This paper first proposes the concept of label enhancement with a formal definition. Then, existing algorithms that can be used for label enhancement have been surveyed, and compared in the experiments. Results of the experiments reveal that label enhancement can effectively discover the difference of the label importance hidden in the data, and improve the performance of multi-label learning.