Multilabel classification is the task of assigning relevant labels to an instance, and it has received considerable attention in recent years. This task can be performed by extending a single-label classifier, such as the naïve Bayes classifier, to utilize the useful relations among labels for achieving better multilabel classification accuracy. However, the conventional multilabel naïve Bayes classifier treats each label independently and hence neglects the relations among labels, resulting in degenerated accuracy. We propose a new multilabel naïve Bayes classifier that considers the relations or dependence among labels. Experimental results show that the proposed method outperforms conventional multilabel classifiers.

多标签分类是为实例分配相关标签的任务，并且近年来受到了相当大的关注。可以通过扩展单标签分类器（例如朴素的贝叶斯分类器）来利用标签之间的有用关系来实现更好的多标签分类准确性，从而执行此任务。然而，传统的多标签朴素贝叶斯分类器独立地对待每个标签，因此忽略了标签之间的关系，导致准确性下降。我们提出了一种新的多标签朴素贝叶斯分类器，该分类器考虑了标签之间的关系或依赖性。实验结果表明，该方法优于传统的多标签分类器。