In multi-label learning, the performance of a learning algorithm can be improved by discovering and making use of the dependencies within the labels. In this paper, an innovated algorithm for multi-label learning based on the classifier chain model is proposed. This algorithm mainly consists of two steps. The dependencies are quantified firstly using mutual information, and then a tree structure of labels is derived to depict the relationship within labels. Thus, the randomness of dependencies in classifier chain is weakened, and the linear dependency is generalized to a tree structure one. To further utilize the dependencies, ensemble technique is used to learn and aggregate multiple trees of labels. The experimental results show that the proposed algorithm is also competitive alternative and it improves the performance significantly after ensemble learning especially, hence it can learn the dependencies within labels more effectively.%在多标记学习中,发现与利用各标记之间的依赖关系能提高学习算法的性能.文中基于分类器链模型提出一种针对性的多标记分类算法.该算法首先量化标记间的依赖程度,并构建标记之间明确的树型依赖结构,从而可减弱分类器链算法中依赖关系的随机性,并将线性依赖关系泛化成树型依赖关系.为充分利用标记间的相互依赖关系,文中采用集成学习技术进一步学习并集成多个不同的标记树型依赖结构.实验结果表明,同分类器链等算法相比,该算法经过集成学习后有更好的分类性能,其能更有效地学习标记间的依赖关系.
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