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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...
We propose a novel semi-supervised boosting algorithm using linear programming, which explicitly maximizes the margin over both labeled and unlabeled data. Experiments conducted on a number of UCI datasets and synthetic data show that, the algorithm we propose performs better than the state-of-the-art supervised and semi-supervised boosting algorithms, and it is more robust with noisy data.
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