A Multi-label Ensemble Method Based on Minimum Ranking Margin Maximization

Multi-label classification is a learning task of predicting a set of target labels for a given example. In this paper, we propose an ensemble method for multi-label classification, which is designed to optimize a novel minimum ranking margin objective function. Moreover, a boosting-type strategy is adopted to construct an accurate multi-label ensemble from multiple weak base classifiers. Experiments on different real-world multi-label classification tasks show that better performance can be achieved compared to other well-established methods.

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