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基于排序互信息的无监督特征选择
引用本文:李纯果,张春琴,李海峰.基于排序互信息的无监督特征选择[J].河北大学学报(自然科学版),2020,40(2):200-204.
作者姓名:李纯果  张春琴  李海峰
作者单位:河北大学数学与信息科学学院,河北保定071002;河北省机器学习与计算智能重点实验室,河北保定071002,河北大学数学与信息科学学院,河北保定071002,河北大学计算机教学部,河北保定071002
基金项目:河北省教育厅项目;国家自然科学基金
摘    要:根据排序问题的单调先验知识,无监督学习问题中的观测属性之间也具备单调关系;否则该属性与排序无关,为冗余属性.基于排序互信息反应的两属性之间的单调关系,提出用每个属性与其他属性之间的平均互信息,来衡量每个属性与排序学习的相关程度,具有最高的平均互信息即为排序最相关的属性.

关 键 词:无监督排序  特征选择  排序互信息  
收稿时间:2019-09-20

Unsupervised feature selection based on ranking mutual information
LI Chunguo,ZHANG Chunqin,LI Haifeng.Unsupervised feature selection based on ranking mutual information[J].Journal of Hebei University (Natural Science Edition),2020,40(2):200-204.
Authors:LI Chunguo  ZHANG Chunqin  LI Haifeng
Institution:1.College of Mathematics and Information Science, Hebei University, Baoding 071002, China; 2.Hebei Key Laboratory of Machine Learning and Computational Intelligence, Baoding 071002, China; 3.Department of Computer Teaching, Hebei University, Baoding 071002, China
Abstract:Based on ranking prior knowledge of monotonicity,each observation attribute should be monotonic with the other observation attributes for unsupervised ranking problems.Otherwise,the attribute would be irrelevant with ranking and should be assumed to a redundant attribute.Based on the ranking mutual information,which reflects the monotonic degree between observation attributes and the order sequence,mean ranking mutual information is proposed to measure the monotonicity between observation attributes.The most relevant attributes should be with the biggest ranking mutual information.
Keywords:unsupervised ranking  feature selection  ranking mutual information  
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