基于标记依赖关系集成分类器链的多示例多标签支持向量机算法

ECC-MIMLSVM⁺是多示例多标签学习框架下一种算法，该算法提出了一种基于分类器链的方法，但其没有充分考虑到标签之间的依赖关系，而且当标签数目的增多，子分类器链长度增加，使得误差传播问题凸显. 因此针对此问题，提出了一种改进算法，将ECC-MIMLSVM⁺算法和标签依赖关系相结合，设计成基于标记依赖关系集成分类器链（ELDCT-MIMLSVM⁺）来加强标签间信息联系，避免信息丢失，提高分类的准确率. 通过实验将本文算法与其他算法进行了对比，实验结果显示，本文算法取得了良好的效果.

Multi-Instance Multi-Label Support Vector Machine Algorithm Based on Labeled Dependency Relation Ensemble Classifier Chain

ECC-MIMLSVM⁺ is an algorithm of multi-instance and multi-label learning framework. This algorithm proposes a method based on classifier chain, but it does not consider the dependencies between labels. When the number of tags increases, the length of the sub classifier chain also increases, making the error propagation problem prominent. Therefore, this paper presents a kind of improved algorithm, combining ECC-MIMLSVM⁺ algorithm and the label dependencies. ELDCT-MIMLSVM⁺ algorithm is designed, which is based on ensembles of label dependencies classifier chain to avoid the information loss and improve the classification accuracy. The experiment results show that the algorithm has good effect.