基于多标记重要性排序的分类器链算法*

多标记分类器链中标记的预测顺序具有随机性,导致学习性能下降,容易造成错误信息的传递.考虑到标记的顺序性,文中提出基于多标记重要性排序的分类器链算法.该算法将标记间相互作用程度的大小作为衡量标记重要程度的依据,在标记相关性的基础上,按照重要性进行标记排序,并将排序结果作为分类器链算法中分类器的顺序,从而解决多标记预测顺序的问题.实验表明,相比现有方法,文中算法在多个数据集上能更稳定有效地分类多标记.

Classifier Chain Algorithm Based on Multi-label Importance Rank

The learning performance of the classifier chain algorithm often decreases due to the random prediction order of multiple labels in the classifier chains. Moreover, the error information is disseminated. With the consideration of the order of labels in a chain, a classifier chain algorithm based on multi-label importance rank is proposed. The information of interaction between the markers is used as a prerequisite to measure the label importance. On the basis of correlation, the labels are sorted according to their importance, and the ranking results are regarded as the classifier order in classifier chain algorithm. Thus, the problem of multi-label prediction sequence is solved. Experiments show that the proposed algorithm is more stable and efficient for multi-label classification compared with some state-of-the-art methods.