Unsupervised feature selection based on ranking mutual information

doi:10.3969/j.issn.1000-1565.2020.02.013

Journal of Hebei University (Natural Science Edition) ›› 2020, Vol. 40 ›› Issue (2): 200-204.DOI: 10.3969/j.issn.1000-1565.2020.02.013

Previous Articles Next Articles

Unsupervised feature selection based on ranking mutual information

LI Chunguo^1,2,ZHANG Chunqin¹,LI Haifeng³

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:2019-09-20 Online:2020-03-25 Published:2020-03-25

Abstract

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.

Key words: unsupervised ranking, feature selection, ranking mutual information

CLC Number:

O235

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.

References

[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.

Unsupervised feature selection based on ranking mutual information

PDF (PC)

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 2

Recommended Articles

Metrics

Comments

[1]	ZHANG Qunfeng. A fuzzy decision tree induction algorithm based on loose lower approximation [J]. Journal of Hebei University (Natural Science Edition), 2016, 36(3): 225-228.
[2]	ZHANG Qunfeng,ZHANG Tianyi. Reducing the attributes of decision tables with real values based on loose lower approximation [J]. Journal of Hebei University (Natural Science Edition), 2013, 33(4): 337-341.