  Multi-label classification has attracted an increasing amount of attention in
recent years. To this end, many algorithms have been developed to classify
multi-label data in an effective manner. However, they usually do not consider
the pairwise relations indicated by sample labels, which actually play
important roles in multi-label classification. Inspired by this, we naturally
extend the traditional pairwise constraints to the multi-label scenario via a
flexible thresholding scheme. Moreover, to improve the generalization ability
of the classifier, we adopt a boosting-like strategy to construct a multi-label
ensemble from a group of base classifiers. To achieve these goals, this paper
presents a novel multi-label classification framework named Variable Pairwise
Constraint projection for Multi-label Ensemble (VPCME). Specifically, we take
advantage of the variable pairwise constraint projection to learn a
lower-dimensional data representation, which preserves the correlations between
samples and labels. Thereafter, the base classifiers are trained in the new
data space. For the boosting-like strategy, we employ both the variable
pairwise constraints and the bootstrap steps to diversify the base classifiers.
Empirical studies have shown the superiority of the proposed method in
comparison with other approaches.
