基于平均期望间隔的多标签分类主动学习方法

针对多标签主动学习速度较慢的问题，提出一种基于平均期望间隔的多标签分类的主动学习方法。计算支持向量机分类器中的期望间隔，并将其作为样本选择标准。实验结果表明，该方法在分类精度、Hamming Loss、Coverage等评价标准上优于基于决策值和后验概率等主动学习策略，能更好地评价未标记样本，有效提高分类精度和速度。

Active Learning Method for Multi-label Classification Based on Average Expectation Margin

Aiming at the problems that active learning in multi-label classification is slowly, this paper proposes an improved method for multi-label classification which based on average expectation margin. The method by calculating Support Vector Maehine（SVM） expectation margin as the selection criteria. Experimental results show that method proposed in this paper outperforms than other active learning strategy based on decision value and posterior probability strategy in terms of classification accuracy or Hamming Loss or Coverage. It can evaluate the unlabeled sample more appropriate, increase the classification accuracy and classification rate more efficient.