Exploiting Label Correlations Using DBN Chains for Multi-Label Classification
Deng-Bao Wang, Fei Hu, Li Li · 2017
In multi-label learning, each instance in the dataset is associated with a set of labels, and the correlations between different labels are important. The existing Classifier Chains transform the multi-label learning into a chain of binary classification and exploit label correlations by extending the feature space with the 0/1 label associations of all previous binary classifiers. In this paper, we exploit label correlations using the hidden layer information in deep networks. We build the deep belief networks(DBN) as a single-label classifier for each class, and extend the feature space for one class with the hidden layer information in the DBN built for other classes. Experiments on real-world multi-label learning problems shows that the DBN Chain structure is highly comparable to the existing method.