基于关联规则和拓扑序列的分类器链方法

Classifier Chains Method Based on Association Rules and Topological Sequences

在分类器链方法中,如何确定标签学习次序至关重要,为此,提出一种基于关联规则和拓扑序列的分类器链方法 (TSECC).首先结合频繁模式设计了一种基于强关联规则的标签依赖度量策略;接下来通过标签间依赖关系构建有向无环图,对图中所有顶点进行拓扑排序;最后将得到的拓扑序列作为分类器链方法中标签的学习次序,对每个标签的分类器依次迭代更新.特别地,为减少无标签依赖或标签依赖度较低的“孤独”标签对其余标签预测性能的影响,将“孤独”标签排在拓扑序列之外,利用二元关联模型训练.在多种公共多标签数据集上的实验结果表明, TSECC能够有效提升分类性能.

The order of label learning is crucial to a classifier chains method.Therefore,this study proposes a classifier chains method based on the association rules and topological sequence(TSECC).Specifically,a measurement strategy for label dependencies based on strong association rules is designed by leveraging frequent patterns.Then,a directed acyclic graph is constructed according to the dependency relationships among the labels to topologically sort all the vertices in the graph.Finally,the topological sequence obtained is used as the order of label learning to iteratively update each label’s classifier successively.In particular,to reduce the impact of“lonely”labels with no or low label dependencies on the prediction performance on the other labels,TSECC excludes“lonely”labels out of the topological sequence and uses a binary relevance model to train them separately.Experimental results on a variety of public multi-label datasets show that TSECC can effectively improve classification performance.