An unbiased method for constructing multilabel classification trees

DOI10.1016/j.csda.2003.10.009zbMath1429.62260OpenAlexW2055150205MaRDI QIDQ956995

B. E. Eshmatov

Publication date: 26 November 2008

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

Full work available at URL: https://doi.org/10.1016/j.csda.2003.10.009

zbMATH Keywords

stopping rulepruningbias in variable selectionmultilabel classification treepower in variable selection

Mathematics Subject Classification ID

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

Related Items (2)

Improving the precision of classification trees ⋮ Unbiased variable selection for classification trees with multivariate responses

Uses Software

• BoosTexter
• C4.5

Cites Work

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• Graph-theoretic measures of multivariate association and prediction
• Multivariate generalizations of the Wald-Wolfowitz and Smirnov two-sample tests
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• BoosTexter: A boosting-based system for text categorization
• Improved boosting algorithms using confidence-rated predictions
• Tree-Structured Classification Via Generalized Discriminant Analysis
• Classification Trees for Multiple Binary Responses
• Modelling multivariate binary data with alternating logistic regressions
• Marginal Regression Models for Clustered Ordinal Measurements
• A likelihood-based method for analysing longitudinal binary responses
• Regression Models for a Bivariate Discrete and Continuous Outcome with Clustering
• Testing the equality of distributions of random vectors with categorical components.

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