In multi-label learning, exploiting label correlation, alleviating class imbalance and learning label-specific features have been hot topics to increase classification performance. In the paper, we propose a method to address the three issues simultaneously. The method, named LPLC-LA, builds a Bayesian model by exploiting the local positive and negative label correlations with label awareness. LPLC-LA consists of extracting label-specific features to obtain the local positive and negative correlation, defining two label aware weights for label imbalance and label separability, and then improving the estimation of label conditional probability through the two weights. The experimental results over eight benchmark datasets show that LPLC-LA can achieve better performance compared with other state-of-the-art approaches.

在多标签学习中，利用标签相关性、缓解类不平衡和学习标签特定的特征一直是提高分类性能的热门话题。在本文中，我们提出了一种同时解决这三个问题的方法。该方法名为 LPLC-LA，通过利用具有标签意识的局部正负标签相关性来构建贝叶斯模型。LPLC-LA 包括提取特定于标签的特征以获得局部正相关和负相关，为标签不平衡和标签可分离性定义两个标签感知权重，然后通过两个权重改进标签条件概率的估计。八个基准数据集的实验结果表明，与其他最先进的方法相比，LPLC-LA 可以实现更好的性能。