Global and local relationships based on multi-label classification algorithm with label-specific features
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NETL
NSTL
万方数据
针对忽视局部关系中的二阶标记关系问题,本文提出了一种基于全局和局部关系的类属特征多标记分类(global and lo-cal relationships based on multi-label classification algorithm with label-specific features,LFGML)算法.通过全局关系的角度来获取类属特征,使用加权平均法计算每个实例的邻域信息,利用杰卡德相似度提取局部关系中的二阶标记关系.LFGML算法在10 个多标记数据集Genbase、Medical、Arts、Health、Flags、Cal500、Yeast、Image、Education和Emotions进行了实验.结果表明,所提出的算法相对于其他对比算法在多标记分类中具有明显的的性能优势.
To address the problem of neglecting the second-order label relation in the local label correlation,we propose a new algorithm called global and local relationships based on multi-label classification algorithm with label-specific features(LFGML).Specifically,the label-specific features are firstly obtained through the perspective of global relations,then the neighbourhood information of each instance is calculated using the weighted average method.The second-order label relationship in the local relationship are extracted using Jaccard similarity.The LFGML algorithm is tested on ten multi-label datasets:Genbase,Medical,Arts,Health,Flags,Cal500,Yeast,Image,Education and Emotions.The results demonstrate that our proposed algorithm outperforms other comparison algorithms in multi-label classification.
NETL
NSTL
万方数据
