信阳师范学院学报（自然科学版）2018，Vol.31Issue(1)：119-123,5.DOI:10.3969/j.issn.1003-0972.2018.01.024
基于随机标记子集的多标记数据流分类算法
Classification for Multi-label Data Streams Based on Random Labelsets
摘要
Abstract
To address the issue of concept drift, on the basis of considering the dependency between labels, a no-vel ensemble classifier was introduced based on random labelsets for multi-label data streams. First, it divided the label set into several subsets based on RAkEL algorithm. Then a classifier on each subset was built using probabilistic classi-fier chain. Moreover, the adaptive windowing algorithm as a change detector was used to deal with concept drift. The experimental results on both synthetic and real-world data streams showed that our method achieves better performance than the previous methods, especially in datasets with concept drifts.关键词
数据流/多标记/集成学习/概念漂移/依赖关系Key words
data streams/multi-label/ensemble learning/concept drift/label dependency分类
信息技术与安全科学引用本文复制引用
孙艳歌,尤磊,卲罕,李艳灵..基于随机标记子集的多标记数据流分类算法[J].信阳师范学院学报（自然科学版）,2018,31(1):119-123,5.基金项目
国家自然科学基金项目(61572417) （61572417）
信阳师范学院青年骨干教师计划(2016GGJS-08) （2016GGJS-08）
