基于排序互信息的无监督特征选择

  • 李纯果 ,
  • 张春琴 ,
  • 李海峰
展开
李纯果(1981—),女,河北邯郸人,河北大学副教授,博士,主要从事统计学习、无监督学习排序、信息论、无监督评价等方面的研究. E-mail: licg@hbu.cn

收稿日期: 2019-09-20

  网络出版日期: 2020-03-25

基金资助

国家自然科学基金资助项目(61573348);河北省教育厅资助项目(ZC2016157)

Unsupervised feature selection based on ranking mutual information

  • LI Chunguo ,
  • ZHANG Chunqin ,
  • LI Haifeng
Expand
  • 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

Received date: 2019-09-20

  Online published: 2020-03-25

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.

本文引用格式

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 . DOI: 10.3969/j.issn.1000-1565.2020.02.013

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.

参考文献

[1] 李纯果,李海峰.无监督排序学习算法的一致性比较[J].河北大学学报(自然科学版),2015,35(2):182-187.DOI: 10.3969/j.issn.1000-1565.2015.02.013.
[2] YU B.Research on information retrieval model based on ontology[J].EURASIP Journal on Wireless Communications and Networking,2019,2019(30)1-8.DOI:10.1186/s13638-019-1354-z.
[3] MA S,GONG C,HU R,et al.Query independent scholarly article ranking[C] //2018 IEEE 34th International Conference on Data Engineering(ICDE),2018:953-964.DOI:10.1109/ICDE.2018.00090.
[4] LAI Y A,HSU C C,CHEN W H,et al.DeepRank: improving unsupervised node ranking via link discovery[J].Data Mining and Knowledge Discovery,2019,33: 474–498.DOI:10.1007/s10618-018-0601-y.
[5] 李纯果.基于主曲线的无监督排序学习及其在综合评价中的应用[D].北京:中国科学院自动化研究所,2015.
[6] 李海峰,李纯果.深度学习结构和算法比较分析[J].河北大学学报(自然科学版),2012,32(5):538-544.DOI: 10.3969/j.issn.1000-1565.2012.05.016.
[7] SCHOLKOPF B,SIMARD P,SMOLA AJ,et al.Prior knowledge in support vector kernels[C] //Twelfth Conference on Neural Information processing Systems,1998:640-646.
[8] VAPNIK V,VASHIST A.A new learning paradigm: learning using privileged information[J].Neural Networks,2009,22(5): 544-557.DOI: 10.1016/j.neunet.2009.06.042.
[9] 胡包钢,王泳,杨双红,等.如何增加人工神经元网络的透明度[J].模式识别与人工智能,2007,20(1): 72-84.DOI: 10.3969/j.issn.1003-6059.2007.01.012.
[10] HU Q H,GUO M Z,YU D R,et al.Information entropy for ordinal classification[J].Science China Information Sciences,2010,53(6): 1188-1200.DOI: 10.1007/s11432-010-3117-7.
[11] HU Q H,CHE X,ZHANG L.Rank entropy-based decision trees for monotonic classification[J].IEEE Transactions on Knowledge and Data Engineering,2012,24(11): 2052-2064.DOI: 10.1109/tkde.2011.149.
[12] 张文修,仇国芳.基于粗糙集的不确定决策[M].北京:清华大学出版社,2005.
文章导航

/

〈 〉