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Feature selection, as a data preprocessing strategy, has been proven to be effective and efficient in preparing data (especially high-dimensional data) for various data mining and machine learning problems.
Variability and mutability, contribution to the study of statistical distribution and relaitons
CW Gini. 1912 · 1912
Earlier work this paper cites.
Direct sparsity optimization based feature selection for multi-class classification. In IJCAI . 1918–1924
Hanyang Peng and Yong Fan. 2016 · 1924
Earlier work this paper cites.
Heterogeneous feature selection with multi-modal deep neural networks and sparse group lasso
Lei Zhao, Qinghua Hu, and Wenwu Wang. 2015 · 1948
Earlier work this paper cites.
A branch and bound algorithm for feature subset selection
Patrenahalli M Narendra and Keinosuke Fukunaga. 1977 · 1977
Earlier work this paper cites.
Statistics and data analysis in geology . Vol. 646
John C Davis and Robert J Sampson. 1986 · 1986
Earlier work this paper cites.
Genetic algorithms in search, optimization, and machine learning
David E Golberg. 1989 · 1989
Earlier work this paper cites.
Enumerate lasso solutions for feature selection. In AAAI . 1985–1991
Satoshi Hara and Takanori Maehara. 2017 · 1991
Earlier work this paper cites.
A practical approach to feature selection. In ICML Workshop . 249–256
Kenji Kira and Larry A Rendell. 1992 · 1992
Earlier work this paper cites.
Feature selection and feature extraction for text categorization. In Proceedings of the Workshop on Speech and Natural Language . 212–217
David D Lewis. 1992 · 1992
Earlier work this paper cites.
Numerical recipes: example book (C)
William T Vetterling, Saul A Teukolsky, and William H Press. 1992 · 1992
Earlier work this paper cites.
Network studies of social influence
Peter V Marsden and Noah E Friedkin. 1993 · 1993
Earlier work this paper cites.
Using mutual information for selecting features in supervised neural net learning
Roberto Battiti. 1994 · 1994
Earlier work this paper cites.
Toward optimal feature selection. In ICML . 284–292
Daphne Koller and Mehran Sahami. 1995 · 1995
Earlier work this paper cites.
Chi2: Feature selection and discretization of numeric attributes. In ICTAI . 388–391
Huan Liu and Rudy Setiono. 1995 · 1995
Earlier work this paper cites.
Regression shrinkage and selection via the lasso
Robert Tibshirani. 1996 · 1996
Earlier work this paper cites.
Wrappers for feature subset selection
Ron Kohavi and George H John. 1997 · 1997
Earlier work this paper cites.
WordNet
Christiane Fellbaum. 1998 · 1998
Earlier work this paper cites.
Feature selection for machine learning: comparing a correlation-based filter approach to the wrapper. In FLAIRS . 235–239
Mark A Hall and Lloyd A Smith. 1999 · 1999
Earlier work this paper cites.
Data visualization and feature selection: new algorithms for nongaussian data. In NIPS . 687–693
Howard Hua Yang and John E Moody. 1999 · 1999
Earlier work this paper cites.
Normalized cuts and image segmentation
Jianbo Shi and Jitendra Malik. 2000 · 2000
Earlier work this paper cites.
Birds of a feather: Homophily in social networks
Miller McPherson, Lynn Smith-Lovin, and James M Cook. 2001 · 2001
Earlier work this paper cites.
Estimating the number of clusters in a data set via the gap statistic
Robert Tibshirani, Guenther Walther, and Trevor Hastie. 2001 · 2001
Earlier work this paper cites.
An introduction to variable and feature selection
Isabelle Guyon and André Elisseeff. 2003 · 2003
Earlier work this paper cites.
Grafting: Fast, incremental feature selection by gradient descent in function space
Simon Perkins, Kevin Lacker, and James Theiler. 2003 · 2003
Earlier work this paper cites.
Online feature selection using grafting. In ICML . 592–599
Simon Perkins and James Theiler. 2003 · 2003
Earlier work this paper cites.
Theoretical and empirical analysis of ReliefF and RReliefF
Marko Robnik-Šikonja and Igor Kononenko. 2003 · 2003
Earlier work this paper cites.
Object recognition with informative features and linear classification. In ICCV . 281–288
Michel Vidal-Naquet and Shimon Ullman. 2003 · 2003
Earlier work this paper cites.
Feature selection for high-dimensional data: a fast correlation-based filter solution. In ICML . 856–863
Lei Yu and Huan Liu. 2003 · 2003
Earlier work this paper cites.
Multiclass spectral clustering. In ICCV . 313–319
Stella X Yu and Jianbo Shi. 2003 · 2003
Earlier work this paper cites.
Convex optimization
Stephen Boyd and Lieven Vandenberghe. 2004 · 2004
Earlier work this paper cites.
Fast binary feature selection with conditional mutual information
François Fleuret. 2004 · 2004
Earlier work this paper cites.
Learning the kernel matrix with semidefinite programming
Gert RG Lanckriet, Nello Cristianini, Peter Bartlett, Laurent El Ghaoui, and Michael I Jordan. 2004 · 2004
Earlier work this paper cites.
1-norm support vector machines. In NIPS . 49–56
Ji Zhu, Saharon Rosset, Robert Tibshirani, and Trevor J Hastie. 2004 · 2004
Earlier work this paper cites.
The elements of statistical learning: data mining, inference and prediction
Trevor Hastie, Robert Tibshirani, Jerome Friedman, and James Franklin. 2005 · 2005
Earlier work this paper cites.
Laplacian score for feature selection. In NIPS . 507–514
Xiaofei He, Deng Cai, and Partha Niyogi. 2005 · 2005
Earlier work this paper cites.
Machine learning based on attribute interactions
Aleks Jakulin. 2005 · 2005
Earlier work this paper cites.
Subband correlation and robust speech recognition
James McAuley, Ji Ming, Darryl Stewart, and Philip Hanna. 2005 · 2005
Earlier work this paper cites.
Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy
Hanchuan Peng, Fuhui Long, and Chris Ding. 2005 · 2005
Earlier work this paper cites.
Sparsity and smoothness via the fused lasso
Robert Tibshirani, Michael Saunders, Saharon Rosset, Ji Zhu, and Keith Knight. 2005 · 2005
Earlier work this paper cites.
Identifying differentially expressed genes from microarray experiments via statistic synthesis
Yee Hwa Yang, Yuanyuan Xiao, and Mark R Segal. 2005 · 2005
Earlier work this paper cites.
Streaming feature selection using alpha-investing. In KDD . 384–393
Jing Zhou, Dean Foster, Robert Stine, and Lyle Ungar. 2005 · 2005
Earlier work this paper cites.
R 1-PCA: rotational invariant ℓ 1 \ell_{1} -norm principal component analysis for robust subspace factorization. In ICML . 281–288
Chris Ding, Ding Zhou, Xiaofeng He, and Hongyuan Zha. 2006 · 2006
Earlier work this paper cites.
Conditional infomax learning: an integrated framework for feature extraction and fusion. In ECCV . 68–82
Dahua Lin and Xiaoou Tang. 2006 · 2006
Earlier work this paper cites.
Spectral clustering for multi-type relational data. In ICML . 585–592
Bo Long, Zhongfei Mark Zhang, Xiaoyun Wu, and Philip S Yu. 2006 · 2006
Earlier work this paper cites.
On the use of variable complementarity for feature selection in cancer classification
Patrick E Meyer and Gianluca Bontempi. 2006 · 2006
Earlier work this paper cites.
Model selection and estimation in regression with grouped variables
Ming Yuan and Yi Lin. 2006 · 2006
Earlier work this paper cites.
A direct formulation for sparse PCA using semidefinite programming
Alexandre d’Aspremont, Laurent El Ghaoui, Michael I Jordan, and Gert RG Lanckriet. 2007 · 2007
Earlier work this paper cites.
On consensus biomarker selection
Janusz Dutkowski and Anna Gambin. 2007 · 2007
Earlier work this paper cites.
Stability of feature selection algorithms: a study on high-dimensional spaces
Alexandros Kalousis, Julien Prados, and Melanie Hilario. 2007 · 2007
Earlier work this paper cites.
The link-prediction problem for social networks
David Liben-Nowell and Jon Kleinberg. 2007 · 2007
Earlier work this paper cites.
Computational methods of feature selection
Huan Liu and Hiroshi Motoda. 2007 · 2007
Earlier work this paper cites.
A probabilistic framework for relational clustering. In KDD . 470–479
Bo Long, Zhongfei Mark Zhang, and Philip S Yu. 2007 · 2007
Earlier work this paper cites.
Supervised group Lasso with applications to microarray data analysis
Shuangge Ma, Xiao Song, and Jian Huang. 2007 · 2007
Earlier work this paper cites.
Classification in networked data: a toolkit and a univariate case study
Sofus A Macskassy and Foster Provost. 2007 · 2007
Earlier work this paper cites.
Joint covariate selection for grouped classification
Guillaume Obozinski, Ben Taskar, and Michael Jordan. 2007 · 2007
Earlier work this paper cites.
A review of feature selection techniques in bioinformatics
Yvan Saeys, Iñaki Inza, and Pedro Larrañaga. 2007 · 2007
Cited alongside, same era.
Trace ratio vs. ratio trace for dimensionality reduction. In CVPR . 1–8
Huan Wang, Shuicheng Yan, Dong Xu, Xiaoou Tang, and Thomas Huang. 2007 · 2007
Cited alongside, same era.
Spectral feature selection for supervised and unsupervised learning. In ICML . 1151–1157
Zheng Zhao and Huan Liu. 2007 · 2007
Cited alongside, same era.
Consistency of the group lasso and multiple kernel learning
Francis R Bach. 2008 · 2008
Cited alongside, same era.
A powerful feature selection approach based on mutual information
Ali El Akadi, Abdeljalil El Ouardighi, and Driss Aboutajdine. 2008 · 2008
Cited alongside, same era.
Feature extraction: foundations and applications
Isabelle Guyon, Steve Gunn, Masoud Nikravesh, and Lofti A Zadeh. 2008 · 2008
Feature selection ensemble
Qiang Shen, Ren Diao, and Pan Su. 2012 · 2012
Later among the works it cites.
Feature grouping and selection over an undirected graph. In KDD . 922–930
Sen Yang, Lei Yuan, Ying-Cheng Lai, Xiaotong Shen, Peter Wonka, and Jieping Ye. 2012 · 2012
Later among the works it cites.
Sparse methods for biomedical data
Jieping Ye and Jun Liu. 2012 · 2012
Later among the works it cites.
Modeling disease progression via fused sparse group lasso. In KDD . 1095–1103
Jiayu Zhou, Jun Liu, Vaibhav A Narayan, and Jieping Ye. 2012 · 2012
Later among the works it cites.
Ensemble methods: foundations and algorithms
Zhi-Hua Zhou. 2012 · 2012
Later among the works it cites.
Feature selection for clustering: a review
Salem Alelyani, Jiliang Tang, and Huan Liu. 2013 · 2013
Later among the works it cites.
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Cited alongside, same era.
The group lasso for logistic regression
Lukas Meier, Sara Van De Geer, and Peter Bühlmann. 2008 · 2008
Cited alongside, same era.
Information-theoretic feature selection in microarray data using variable complementarity
Patrick Emmanuel Meyer, Colas Schretter, and Gianluca Bontempi. 2008 · 2008
Cited alongside, same era.
Trace ratio criterion for feature selection. In AAAI . 671–676
Feiping Nie, Shiming Xiang, Yangqing Jia, Changshui Zhang, and Shuicheng Yan. 2008 · 2008
Cited alongside, same era.
Robust feature selection using ensemble feature selection techniques
Yvan Saeys, Thomas Abeel, and Yves Van de Peer. 2008 · 2008
Cited alongside, same era.
Collective classification in network data
Prithviraj Sen, Galileo Namata, Mustafa Bilgic, Lise Getoor, Brian Galligher, and Tina Eliassi-Rad. 2008 · 2008
Cited alongside, same era.
Flexible latent variable models for multi-task learning
Jian Zhang, Zoubin Ghahramani, and Yiming Yang. 2008 · 2008
Cited alongside, same era.
Exact top-k feature selection via ℓ 2 , 0 \ell_{2,0} -norm constraint. In IJCAI . 1240–1246
Xiao Cai, Feiping Nie, and Heng Huang. 2013 · 2013
Later among the works it cites.
Local and global discriminative learning for unsupervised feature selection. In ICDM . 131–140
Liang Du, Zhiyong Shen, Xuan Li, Peng Zhou, and Yi-Dong Shen. 2013 · 2013
Later among the works it cites.
Adaptive unsupervised multi-view feature selection for visual concept recognition
Yinfu Feng, Jun Xiao, Yueting Zhuang, and Xiaoming Liu. 2013 · 2013
Later among the works it cites.
Introduction to statistical pattern recognition
Keinosuke Fukunaga. 2013 · 2013
Later among the works it cites.
ActNeT: active learning for networked texts in microblogging. In SDM . 306–314
Xia Hu, Jiliang Tang, Huiji Gao, and Huan Liu. 2013 · 2013
Later among the works it cites.
Pass-efficient unsupervised feature selection. In NIPS . 1628–1636
Crystal Maung and Haim Schweitzer. 2013 · 2013
Later among the works it cites.
Robust unsupervised feature selection. In IJCAI . 1621–1627
Mingjie Qian and Chengxiang Zhai. 2013 · 2013
Later among the works it cites.
Unsupervised feature selection for multi-view data in social media. In SDM . 270–278
Jiliang Tang, Xia Hu, Huiji Gao, and Huan Liu. 2013 · 2013
Later among the works it cites.
Coselect: Feature selection with instance selection for social media data. In SDM . 695–703
Jiliang Tang and Huan Liu. 2013 · 2013
Later among the works it cites.
Multi-view clustering and feature learning via structured sparsity. In ICML . 352–360
Hua Wang, Feiping Nie, and Heng Huang. 2013 · 2013
Later among the works it cites.
Massively parallel feature selection: an approach based on variance preservation
Zheng Zhao, Ruiwen Zhang, James Cox, David Duling, and Warren Sarle. 2013 · 2013
Later among the works it cites.
A survey on feature selection methods
Girish Chandrashekar and Ferat Sahin. 2014 · 2014
Later among the works it cites.
A convex formulation for semi-supervised multi-label feature selection. In AAAI . 1171–1177
Xiaojun Chang, Feiping Nie, Yi Yang, and Heng Huang. 2014 · 2014
Later among the works it cites.
Global and local structure preservation for feature selection
Xinwang Liu, Lei Wang, Jian Zhang, Jianping Yin, and Huan Liu. 2014 · 2014
Later among the works it cites.
Effective global approaches for mutual information based feature selection. In KDD . 512–521
Xuan Vinh Nguyen, Jeffrey Chan, Simone Romano, and James Bailey. 2014 · 2014
Later among the works it cites.
Robust spectral learning for unsupervised feature selection. In ICDM . 977–982
Lei Shi, Liang Du, and Yi-Dong Shen. 2014 · 2014
Later among the works it cites.
Towards ultrahigh dimensional feature selection for big data
Mingkui Tan, Ivor W Tsang, and Li Wang. 2014 · 2014
Later among the works it cites.
Feature selection for classification: a review
Jiliang Tang, Salem Alelyani, and Huan Liu. 2014a · 2014
Later among the works it cites.
Online feature selection and its applications
Jialei Wang, Peilin Zhao, Steven CH Hoi, and Rong Jin. 2014b · 2014
Later among the works it cites.
Gradient boosted feature selection. In KDD . 522–531
Zhixiang Xu, Gao Huang, Kilian Q Weinberger, and Alice X Zheng. 2014 · 2014
Later among the works it cites.
N3LARS: minimum redundancy maximum relevance feature selection for large and high-dimensional data
Makoto Yamada, Avishek Saha, Hua Ouyang, Dawei Yin, and Yi Chang. 2014 · 2014
Later among the works it cites.
Towards scalable and accurate online feature selection for big data. In ICDM . 660–669
Kui Yu, Xindong Wu, Wei Ding, and Jian Pei. 2014 · 2014
Later among the works it cites.
Feature selection at the discrete limit. In AAAI . 1355–1361
Miao Zhang, Chris HQ Ding, Ya Zhang, and Feiping Nie. 2014 · 2014
Later among the works it cites.
Unsupervised feature selection with adaptive structure learning. In KDD . 209–218
Liang Du and Yi-Dong Shen. 2015 · 2015
Later among the works it cites.
Unsupervised feature selection on data streams. In CIKM . 1031–1040
Hao Huang, Shinjae Yoo, and S Kasiviswanathan. 2015 · 2015
Later among the works it cites.
Feature selection using deep neural networks. In IJCNN . 1–6
Debaditya Roy, K Sri Rama Murty, and C Krishna Mohan. 2015 · 2015
Later among the works it cites.
Multi-layer feature reduction for tree structured group lasso via hierarchical projection. In NIPS . 1279–1287
Jie Wang and Jieping Ye. 2015 · 2015
Later among the works it cites.
Efficient partial order preserving unsupervised feature selection on networks. In SDM . 82–90
Xiaokai Wei, Sihong Xie, and Philip S Yu. 2015 · 2015
Later among the works it cites.
Towards mining trapezoidal data streams. In ICDM . 1111–1116
Qin Zhang, Peng Zhang, Guodong Long, Wei Ding, Chengqi Zhang, and Xindong Wu. 2015 · 2015
Later among the works it cites.
Supervised, unsupervised, and semi-supervised feature selection: a review on gene selection
Jun Chin Ang, Andri Mirzal, Habibollah Haron, and Haza Nuzly Abdull Hamed. 2016 · 2016
Closest in time.
Unsupervised feature selection by heuristic search with provable bounds on suboptimality. In AAAI . 666–672
Hiromasa Arai, Crystal Maung, Ke Xu, and Haim Schweitzer. 2016 · 2016
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FASCINATE: fast cross-layer dependency inference on multi-layered networks. In KDD . 765–774
Chen Chen, Hanghang Tong, Lei Xie, Lei Ying, and Qing He. 2016 · 2016
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FeatureMiner: a tool for interactive feature selection. In CIKM . 2445–2448
Kewei Cheng, Jundong Li, and Huan Liu. 2016 · 2016
Closest in time.
Variational information maximization for feature selection. In NIPS . 487–495
Shuyang Gao, Greg Ver Steeg, and Aram Galstyan. 2016 · 2016
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Multi-label informed feature selection. In IJCAI . 1627–1633
Ling Jian, Jundong Li, Kai Shu, and Huan Liu. 2016 · 2016
Closest in time.
Unsupervised feature selection with structured graph optimization. In AAAI . 1302–1308
Feiping Nie, Wei Zhu, Xuelong Li, et al · 2016
Closest in time.
Efficient high-order interaction-aware feature selection based on conditional mutual information. In NIPS . 4637–4645
Alexander Shishkin, Anastasia Bezzubtseva, Alexey Drutsa, Ilia Shishkov, Ekaterina Gladkikh, Gleb Gusev, and Pavel Serdyukov. 2016 · 2016
Closest in time.
Unsupervised feature selection by preserving stochastic neighbors. In AISTATS . 995–1003
Xiaokai Wei and Philip S Yu. 2016 · 2016
Closest in time.
Graph regularized feature selection with data reconstruction
Zhou Zhao, Xiaofei He, Deng Cai, Lijun Zhang, Wilfred Ng, and Yueting Zhuang. 2016 · 2016
Closest in time.
Coupled dictionary learning for unsupervised feature selection. In AAAI . 2422–2428
Pengfei Zhu, Qinghua Hu, Changqing Zhang, and Wangmeng Zuo. 2016 · 2016
Closest in time.
Unsupervised feature selection in signed social networks. In KDD . 777–786
Kewei Cheng, Jundong Li, and Huan Liu. 2017 · 2017
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Challenges of feature selection for big data analytics
Jundong Li and Huan Liu. 2017 · 2017
Closest in time.
A general framework for sparsity regularized feature selection via iteratively reweighted least square minimization. In AAAI . 2471–2477
Hanyang Peng and Yong Fan. 2017 · 2017
Closest in time.
Gleaning wisdom from the past: Early detection of emerging rumors in social media. In SDM . SIAM, 99–107
Liang Wu, Jundong Li, Xia Hu, and Huan Liu. 2017 · 2017
Closest in time.
Scalable feature selection via distributed diversity maximization. In AAAI . 2876–2883
Sepehr Abbasi Zadeh, Mehrdad Ghadiri, Vahab S Mirrokni, and Morteza Zadimoghaddam. 2017 · 2017
Closest in time.
A Randomized Approach for Crowdsourcing in the Presence of Multiple Views. In ICDM
Yao Zhou and Jingrui He. 2017 · 2017
Closest in time.
Ultrahigh dimensional feature selection: beyond the linear model
Jianqing Fan, Richard Samworth, and Yichao Wu. 2009 · 2038
Closest in time.
Attentional neural network: Feature selection using cognitive feedback. In NIPS . 2033–2041
Qian Wang, Jiaxing Zhang, Sen Song, and Zheng Zhang. 2014a · 2041
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