Multi-label text categorization with joint learning predictions-as-features method

Multi-label text categorization is a type of text categorization, where each document is assigned to one or more categories. Recently, a series of methods have been developed, which train a classifier for each label, organize the classifiers in a partially ordered structure and take predictions produced by the former classifiers as the latter classifiers' features. These predictions-asfeatures style methods model high order label dependencies and obtain high performance. Nevertheless, the predictionsas-features methods suffer a drawback. When training a classifier for one label, the predictions-as-features methods can model dependencies between former labels and the current label, but they can't model dependencies between the current label and the latter labels. To address this problem, we propose a novel joint learning algorithin that allows the feedbacks to be propagated from the classifiers for latter labels to the classifier for the current label. We conduct experiments using real-world textual data sets, and these experiments illustrate the predictions-as-features models trained by our algorithm outperform the original models.