Variable selection for binary classification in large dimensions: comparisons and application to microarray data

zbMath1455.62124MaRDI QIDQ2197385

Badih Ghattas, Anis Ben Ishak

Publication date: 31 August 2020

Published in: Journal de la Société Française de Statistique \& Revue de Statistique Appliquée (Search for Journal in Brave)

Full work available at URL: http://www.numdam.org/item/JSFS_2008__149_3_43_0

zbMATH Keywords

bootstrapsupport vector machinesrandom forestscross validationfeature selectionmicroarray dataforward selectionranking rulesGLMpathSVM-based criteria

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Generalized linear models (logistic models) (62J12) Learning and adaptive systems in artificial intelligence (68T05)

Uses Software

• GeneSrF

Cites Work

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• Wrappers for feature subset selection
• Least angle regression. (With discussion)
• Comparison of Discrimination Methods for the Classification of Tumors Using Gene Expression Data
• Selection bias in gene extraction on the basis of microarray gene-expression data
• 10.1162/153244303322753616
• 10.1162/153244303322753706
• 10.1162/153244303322753715
• 10.1162/153244303322753751
• Random forests
• Gene selection for cancer classification using support vector machines

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