基于全局和局部关系的类属特征多标记分类算法
Global and local relationships based on multi-label classification algorithm with label-specific features
张珊丹 1翁伟 2谢小竹 1魏博文 1王劲波 3文娟3
作者信息
- 1. 厦门理工学院计算机与信息工程学院,福建 厦门 361024
- 2. 厦门理工学院计算机与信息工程学院,福建 厦门 361024;福建省模式识别与图像理解重点实验室,福建 厦门 361024
- 3. 厦门大学经济学院,福建 厦门 361005
- 折叠
摘要
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.
Abstract
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.
关键词
多标记学习/全局关系/局部关系/类属特征/优化Key words
multi-label learning/global label correlation/local label correlation/label-specific feature/optimization引用本文复制引用
出版年
2024
NETL
NSTL
万方数据