Selection of Binary Variables and Classification by Boosting

DOI10.1080/03610910701419729zbMath1126.62054OpenAlexW2051730204MaRDI QIDQ5436402

Junyong Park, Jayson D. Wilbur, Corinne Ackerman, Cindy H. Nakatsu, Jayanta K. Ghosh

Publication date: 16 January 2008

Published in: Communications in Statistics - Simulation and Computation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/03610910701419729

zbMATH Keywords

thresholdinghigh-dimensional datavariable selectionboostingcross validationmultivariate binary dataDNA fingerprints

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Applications of statistics to biology and medical sciences; meta analysis (62P10)

Uses Software

• ElemStatLearn

Cites Work

• A decision-theoretic generalization of on-line learning and an application to boosting
• Boosting the margin: a new explanation for the effectiveness of voting methods
• Additive logistic regression: a statistical view of boosting. (With discussion and a rejoinder by the authors)
• Population theory for boosting ensembles.
• Process consistency for AdaBoost.
• Statistical behavior and consistency of classification methods based on convex risk minimization.
• Variable Selection in High-Dimensional Multivariate Binary Data with Application to the Analysis of Microbial Community DNA Fingerprints
• Comparison of Discrimination Methods for the Classification of Tumors Using Gene Expression Data
• The elements of statistical learning. Data mining, inference, and prediction

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