重庆邮电大学学报（自然科学版）2017，Vol.29Issue(6)：776-784,9.DOI:10.3979/j.issn.1673-825X.2017.06.011
基于邻域粗糙集的主动学习方法
Algorithm for active learning based on neighbor rough set theory
摘要
Abstract
Active learning is one of the major research directions of machine learning.Most active learning approaches select uncertain or representative unlabeled samples to query their labels,and then add them into labeled data sets for classifier learning.However,these approaches have not fully utilized data distribution information,and not processed outlier acquisition problem well enough,too.With neighbor rough set theory,an algorithm named NRS-AL is proposed.The experiment results have shown that in UCI data set,combined with uncertainty and representative calculation of samples,the proposed algorithm in this paper has solved the previous problems,and is effective in solving sample choosing problems in active learning,which shows better accuracy and AUC performances than others in the literatures.关键词
邻域粗糙集/主动学习/基于池的样本选择Key words
neighborhood rough set/active learning/pool-based sample selection分类
信息技术与安全科学引用本文复制引用
胡峰,周耀,王蕾..基于邻域粗糙集的主动学习方法[J].重庆邮电大学学报（自然科学版）,2017,29(6):776-784,9.基金项目
国家自然科学基金(61309014) （61309014）
教育部人文社科规划项目(15XJA630003) （15XJA630003）
重庆市教委科学技术研究项目(KJ1500416) （KJ1500416）
重庆市基础与前沿研究计划项目(cstc2013jcyjA40063)The National Natural Science Fundation of China (61309014) （cstc2013jcyjA40063）
The Ministry of Education Humanities and Social Sciences Program(15XJA630003) （15XJA630003）
The Science and Technology Research Project of Chongqing Municipal Education Commission (KJ1500416) （KJ1500416）
The Chongqing Basic and Frontier Research Program(cstc2013jcyjA40063) （cstc2013jcyjA40063）
