Unbiased variable selection for classification trees with multivariate responses

DOI10.1016/j.csda.2006.02.015zbMath1157.62438OpenAlexW2089227745MaRDI QIDQ1010402

Tzu-Haw Lee, Yu-Shan Shih

Publication date: 6 April 2009

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.csda.2006.02.015

zbMATH Keywords

powerselection biasconditional independence testloglinear model

Mathematics Subject Classification ID

Nonparametric hypothesis testing (62G10) Classification and discrimination; cluster analysis (statistical aspects) (62H30) Contingency tables (62H17)

Related Items (3)

Variable selection by random forests using data with missing values ⋮ Improving the precision of classification trees ⋮ Editorial: Statistical learning methods including dimensionality reduction

Uses Software

• R
• C4.5
• bindata

Cites Work

• An unbiased method for constructing multilabel classification trees
• Multivariate data analysis and modeling through classification and regression trees
• Classification Trees for Multiple Binary Responses
• Testing the equality of distributions of random vectors with categorical components.
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