SEQUENTIAL, BOTTOM-UP VARIABLE SELECTION FOR HIGH-DIMENSIONAL CLASSIFICATION

Australian & New Zealand Journal of Statistics ◽

10.1111/j.1467-842x.2010.00594.x ◽

2010 ◽

Vol 52 (4) ◽

pp. 403-421

Author(s):

Peter Hall ◽

Hugh Miller

Keyword(s):

Variable Selection ◽

High Dimensional ◽

Bottom Up ◽

Dimensional Classification ◽

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Feature selection for high-dimensional classification using a competitive swarm optimizer

Soft Computing ◽

10.1007/s00500-016-2385-6 ◽

2016 ◽

Vol 22 (3) ◽

pp. 811-822 ◽

Author(s):

Shenkai Gu ◽

Ran Cheng ◽

Yaochu Jin

Keyword(s):

Feature Selection ◽

High Dimensional ◽

Dimensional Classification ◽

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Variable Selection for Correlated High-Dimensional Data with Infrequent Categorical Variables: Based on Sparse Sample Regression and Anomaly Detection Technology

Intelligent Decision Technologies - Smart Innovation, Systems and Technologies ◽

10.1007/978-981-16-2765-1_9 ◽

2021 ◽

pp. 109-125

Author(s):

Yuhei Kotsuka ◽

Sumika Arima

Keyword(s):

Variable Selection ◽

Anomaly Detection ◽

High Dimensional Data ◽

Categorical Variables ◽

High Dimensional ◽

Detection Technology ◽

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Variable selection for high-dimensional varying coefficient partially linear models via nonconcave penalty

Metrika ◽

10.1007/s00184-012-0422-8 ◽

2012 ◽

Vol 76 (7) ◽

pp. 887-908 ◽

Author(s):

Zhaoping Hong ◽

Yuao Hu ◽

Heng Lian

Keyword(s):

Variable Selection ◽

Linear Models ◽

High Dimensional ◽

Partially Linear Models ◽

Varying Coefficient ◽

Partially Linear ◽

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Bayesian variable selection for high dimensional generalized linear models: Convergence rates of the fitted densities

The Annals of Statistics ◽

10.1214/009053607000000019 ◽

2007 ◽

Vol 35 (4) ◽

pp. 1487-1511 ◽

Author(s):

Wenxin Jiang

Keyword(s):

Variable Selection ◽

Generalized Linear Models ◽

Linear Models ◽

Convergence Rates ◽

Bayesian Variable Selection ◽

High Dimensional ◽

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Variable selection for high dimensional Gaussian copula regression model: An adaptive hypothesis testing procedure

Computational Statistics & Data Analysis ◽

10.1016/j.csda.2018.03.003 ◽

2018 ◽

Vol 124 ◽

pp. 132-150 ◽

Author(s):

Yong He ◽

Xinsheng Zhang ◽

Liwen Zhang

Keyword(s):

Variable Selection ◽

Regression Model ◽

Hypothesis Testing ◽

Testing Procedure ◽

Gaussian Copula ◽

High Dimensional ◽

Hypothesis Testing Procedure ◽

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Sparse Bayesian variable selection for classifying high-dimensional data

Statistics and Its Interface ◽

10.4310/sii.2018.v11.n2.a14 ◽

2018 ◽

Vol 11 (2) ◽

pp. 385-395 ◽

Author(s):

Aijun Yang ◽

Heng Lian ◽

Xuejun Jiang ◽

Pengfei Liu

Keyword(s):

Variable Selection ◽

High Dimensional Data ◽

Bayesian Variable Selection ◽

High Dimensional ◽

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Bayesian Multiresolution Variable Selection for Ultra-High Dimensional Neuroimaging Data

IEEE/ACM Transactions on Computational Biology and Bioinformatics ◽

10.1109/tcbb.2015.2440244 ◽

2018 ◽

Vol 15 (2) ◽

pp. 537-550

Author(s):

Yize Zhao ◽

Jian Kang ◽

Qi Long

Keyword(s):

Variable Selection ◽

High Dimensional ◽

Neuroimaging Data ◽

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Random KNN modeling and variable selection for high dimensional data

10.33915/etd.4492 ◽

2009 ◽

Author(s):

Shengqiao Li

Keyword(s):

Variable Selection ◽

High Dimensional Data ◽

High Dimensional ◽

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Variable selection for model-based high-dimensional clustering

Prediction and Discovery - Contemporary Mathematics ◽

10.1090/conm/443/08562 ◽

2007 ◽

pp. 177-192

Author(s):

Sijian Wang ◽

Ji Zhu

Keyword(s):

Variable Selection ◽

High Dimensional ◽

Model Based ◽

Selection For ◽

High Dimensional Clustering

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Variable selection for ultra-high-dimensional logistic models

Perspectives on Big Data Analysis - Contemporary Mathematics ◽

10.1090/conm/622/12436 ◽

2014 ◽

pp. 141-158

Author(s):

Pang Du ◽

Pan Wu ◽

Hua Liang

Keyword(s):

Variable Selection ◽

Logistic Models ◽

High Dimensional ◽

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