Learning with Latent Label Hierarchy from Incomplete Multi-Label Data
Yuanli Pei, Xiaoli Z. Fern, Raviv Raich · 2018
Exploiting hierarchical label structure for multi-label classification can significantly improve classification performance and also benefit the labeling process. Existing work either can not make use of such structure or assume the hierarchy is given as a prior. In practice, such hierarchy is not always available beforehand and it is desirable to learn it from data. Moreover, the labels in the training data may be incomplete due to inconsistent labeling process, which raises another learning challenge. This paper studies multi-label learning with a latent label hierarchy and incomplete label assignments. Our goal is to simultaneously learn the hierarchy as well as a multi-label classifier given the input features and incomplete label assignments. We propose a probabilistic model that captures the hierarchical structure and the incompleteness of the labels and introduce an Expectation-Maximization (EM) procedure for maximum likelihood estimation.