Expede Herculem: Learning Multi Labels From Single Label

Abstract

Although there has been a lot of research in multi-label learning task, little attention has been paid on the weak label problem, in which only a subset of labels has been assigned to each instance in the training set. The extreme form of weak label learning is to predict all the labels from just one label set in the training phase. In this paper, we focus on dealing with this kind of weak label learning task, which is commonly met in old legacy information system, and it is also called “Hercules Learning.” We propose a label-group-optimization-based Hercules learning algorithm, which divides the entire label set into multiple groups according to the classifier’s capability to distinguish them, so for each group, we can train a classifier which can predict instance’s label within the group with high accuracy. The experimental results show that our algorithm is obviously superior to the existing weak label learning algorithm.