一种面向多标签分类的在线主动学习算法

doi:10.6040/j.issn.1672-3961.0.2021.316

摘要/Abstract

摘要： 针对现有算法多标签分类器收敛效率低和标签查询策略未考虑特征辨别能力的弊端,提出一种基于判别采样和镜像梯度下降规则的多标签在线主动学习算法(multi-label active mirror descent by discrimination sampling,MLAMD_D)。MLAMD_D算法采用二元关联策略将包含C个标签的多标签分类问题分解成C个相互独立的二分类问题,算法使用镜像梯度下降规则更新其二分类器,并采用基于判别的采样策略。将MLAMD_D算法与现有算法以及基于随机采样和镜像梯度下降规则的多标签在线主动学习算法(multi-label active mirror descent by random sampling,MLAMD_R)在6个多标签分类数据集上进行对比试验。试验结果表明,MLAMD_D算法的多标签分类性能优于其他多标签在线主动学习算法。因此,MLAMD_D算法在处理多标签在线主动学习的任务中具有可行性和有效性。

关键词: 在线主动学习, 多标签分类, 弱监督学习, 基于判别的采样策略, 二元关联策略

中图分类号:

TP181

龚楷伦,翟婷婷,唐鸿成. 一种面向多标签分类的在线主动学习算法[J]. 山东大学学报 (工学版), 2022, 52(2): 80-88.

GONG Kailun, ZHAI Tingting, TANG Hongcheng. An online active learning algorithm for multi-label classification[J]. Journal of Shandong University(Engineering Science), 2022, 52(2): 80-88.

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