Reference health

Random Feature Selection Using Random Subspace Logistic Regression

https://doi.org/10.2139/ssrn.4137571
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36/36 checkable references clean · checked 2026-08-27

Every reference with a DOI in the deposited reference list resolved to a known work in Crossref or DataCite at the dated check, and none carried a retraction, withdrawal, or removal notice.

32 without a DOI — not checked. A reference deposited without a DOI is never matched by title or guessed at; it stays outside the checked set, and this line discloses that.

The 36 checked references that resolve
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Non-parametric classifier-independent feature selection
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Empirical study of feature selection methods based on individual feature evaluation for classification problems
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The application of GIS-based logistic regression for landslide susceptibility mapping in the Kakuda-Yahiko Mountains, Central Japan
resolves10.1007/s11633-009-0062-2
Effective and efficient feature selection for large-scale data using Bayes’ theorem
resolves10.1109/ICCV.2001.937619
Feature selection from huge feature sets
resolves10.1007/978-3-319-21858-8
Feature Selection for High-Dimensional Data
resolves10.1016/j.jeconom.2019.01.009
Forecasting using random subspace methods
resolves10.1016/j.jcv.2005.06.002
Distinguishing dengue fever from other infections on the basis of simple clinical and laboratory features: Application of logistic regression analysis
resolves10.1109/LGRS.2006.877949
Logistic Regression for Feature Selection and Soft Classification of Remote Sensing Data
resolves10.1109/TNB.2005.853657
Multiple SVM-RFE for Gene Selection in Cancer Classification With Expression Data
resolves10.1186/1472-6947-5-3
Comparison of artificial neural network and logistic regression models for prediction of mortality in head trauma based on initial clinical data
resolves10.1023/A:1012487302797
Gene Selection for Cancer Classification using Support Vector Machines
resolves10.1109/TKDE.2003.1245283
Benchmarking attribute selection techniques for discrete class data mining
resolves10.1109/3477.990877
The ANNIGMA-wrapper approach to fast feature selection for neural nets
resolves10.1007/s10479-017-2445-z
Embedded variable selection method using signomial classification
resolves10.1134/S199508021809010X
Quadratic Programming Optimization with Feature Selection for Nonlinear Models
resolves10.1007/978-1-4614-7138-7
An Introduction to Statistical Learning
resolves10.1016/j.jbi.2020.103591
Detection and classification of breast cancer using logistic regression feature selection and GMDH classifier
resolves10.1023/A:1008280620621
Overcoming the Myopia of Inductive Learning Algorithms with RELIEFF
resolves10.1080/01431160412331331012
Application of logistic regression model and its validation for landslide susceptibility mapping using GIS and remote sensing data
resolves10.1109/MIS.2017.38
Challenges of Feature Selection for Big Data Analytics
resolves10.1145/3136625
Feature Selection
resolves10.1093/bib/bbn027
Penalized feature selection and classification in bioinformatics
resolves10.1016/j.ins.2009.02.014
A wrapper method for feature selection using Support Vector Machines
resolves10.3390/s21165571
A Tri-Stage Wrapper-Filter Feature Selection Framework for Disease Classification
resolves10.1109/JSTSP.2008.923858
Information-Theoretic Feature Selection in Microarray Data Using Variable Complementarity
resolves10.1016/j.procs.2016.07.111
A Survey on Feature Selection
resolves10.1109/ISDA.2006.128
Correlation-based Feature Selection Strategy in Neural Classification
resolves10.1007/BF00116251
Induction of decision trees
resolves10.1023/A:1025667309714
Theoretical and Empirical Analysis of ReliefF and RReliefF
resolves10.1093/bioinformatics/btm344
A review of feature selection techniques in bioinformatics
resolves10.1137/1.9781611972795.100
Parallel Large Scale Feature Selection for Logistic Regression
resolves10.1016/j.eswa.2006.10.010
Feature selection for the SVM: An application to hypertension diagnosis
resolves10.1257/jep.28.2.3
Big Data: New Tricks for Econometrics
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Multivariate selection of genetic markers in diagnostic classification
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The Adaptive Lasso and Its Oracle Properties
The 32 references without a DOI — listed, not checked
no DOI — not checkedref5
no DOI — not checkedModelling small-business credit scoring by using logistic regression, neural networks and decision trees
no DOI — not checkedApplication of the logistic function to bio-assay
no DOI — not checkedConditional likelihood maximisation: A unifying framework for information theoretic feature selection
no DOI — not checkedAn adaptive multiple feature subset method for feature ranking and selection
no DOI — not checkedref14
no DOI — not checkedSelecting critical features for data classification based on machine learning methods
no DOI — not checkedFeature selection for clustering
no DOI — not checkedBit-climbing, representational bias, and test suit design
no DOI — not checkedFast binary feature selection with conditional mutual information
no DOI — not checkedAn extensive empirical study of feature selection metrics for text classification
no DOI — not checkedGeneralized fisher score for feature selection
no DOI — not checkedAn incremental approach to contributionbased feature selection
no DOI — not checkedAn introduction to variable and feature selection
no DOI — not checkedref27
no DOI — not checkedref29
no DOI — not checkedLaplacian score for feature selection
no DOI — not checkedA review of feature selection and feature extraction methods applied on microarray data
no DOI — not checkedStability of feature selection algorithm: A review
no DOI — not checkedA practical approach to feature selection
no DOI — not checkedFeature selection and feature extraction for text categorization
no DOI — not checkedToward integrating feature selection algorithms for classification and clustering
no DOI — not checkedParsimonious network design and feature selection through node pruning
no DOI — not checkedFeature selection, l 1 vs. l 2 regularization, and rotational invariance
no DOI — not checkedOpportunities and challenges: Lessons from analyzing terabytes of scanner data
no DOI — not checkedFeature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy
no DOI — not checkedref56
no DOI — not checkedFeature selection for classification: A review
no DOI — not checkedFeature Selection for SVMs
no DOI — not checkedData visualization and feature selection: New algorithms for nongaussian data
no DOI — not checkedFeature selection for high-dimensional data: A fast correlation-based filter solution
no DOI — not checkedA cluster-based sequential feature selection algorithm
What this badge says. CiteStamped means the CHECKABLE references of this work were clean at the dated check: each resolved to a known work in a public registry, and none carried a retraction notice at that time. It says nothing about the quality, findings, or importance of the work itself, and nothing about references deposited without a DOI.

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