We propose a novel cost-sensitive multi-label classification algorithm called cost-sensitive random pair encoding (CSRPE). CSRPE reduces the cost-sensitive multi-label classification problem to many cost-sensitive binary classification problems through the label powerset approach followed by the classic one-versus-one decomposition. While such a naive reduction results in exponentially-many classifiers, we resolve the training challenge of building the many classifiers by random sampling, and the prediction challenge of voting from the many classifiers by nearest-neighbor decoding through casting the one-versus-one decomposition as a special case of error-correcting code. Extensive experimental results demonstrate that CSRPE achieves stable convergence and reaches better performance than other ensemble-learning and error-correcting-coding algorithms for multi-label classification. The results also justify that CSRPE is competitive with state-of-the-art cost-sensitive multi-label classification algorithms for cost-sensitive multi-label classification.

该研究论文介绍了一种基于参考标签向量的经济敏感多标签分类算法，其中利用了集群编码、权重训练和投票解码等步骤，以及嵌入于其码空间中的成本信息，提出了一种新颖的经济敏感主动学习算法，实验结果表明，该算法优于现有算法。

多标记学习的代价敏感参考对编码