关键词: 多标记学习, 样例相关性, 聚类, k近邻

Abstract: An instance’s label information may provide some useful information for the other instances, especially in the case of relatively scarce data. Using the relationship between the labeled and unlabeled instances can avoid the errors caused by insufficient data. Based on the research on instance correlation, an algorithm with local label information for multi-label learning is proposed. This algorithm gets the local label information of the instance firstly, and then takes them into the attribute space to build the new space. Finally, the algorithm classifies the instance based on the new space. Experimental results show that, the new algorithm outperforms the other commonly used multi-label algorithms most of the time.

Key words: multi-label learning, instance correlation, cluster, kNN