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Multi-label performance evaluation metrics could be mainly grouped into two parts: ranking-based and instance-based. The former is based on discriminant function values (e.g., average precision and ranking loss). The latter is associated with predicted relevant label subsets (e.g., Hamming loss and accuracy), which is determined via a proper threshold from the discriminant function values. Firstly,...
For multi-label classification, problem transform algorithms have received more attention due to their good performance and low computational complexity. But how to speed up training and test procedures is still a challenging issue. In this paper, one-by-one data decomposition trick is adopted to divide a k-label problem into k sub-problems, where a specific sub-problem only consists of instances...
Combing one-versus-one decomposition strategy with support vector machine has become an efficient means for multi-label classification problem. But how to speed up its training and test procedures is still a challenging issue. In this paper, we generalize the primary binary support vector machine to construct a double label support vector machine through locating double label instances at marginal...
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