A new robust classifier on noise domains: bagging of Credal C4.5 trees

DOI10.1155/2017/9023970zbMath1380.68327OpenAlexW2775425729WikidataQ62039184 ScholiaQ62039184MaRDI QIDQ1694254

Carlos J. Mantas, Joaquín Abellán, Javier G. Castellano

Publication date: 1 February 2018

Published in: Complexity (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1155/2017/9023970

zbMATH Keywords

data miningknowledge extractionCC4.5 modelRandom Forest (RF) classificationrobust classifiers

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Theory of languages and software systems (knowledge-based systems, expert systems, etc.) for artificial intelligence (68T35)

Uses Software

• UCI-ml
• KEEL
• C4.5

Cites Work

• Bagging predictors
• A Mathematical Theory of Communication
• Building classification trees using the total uncertainty criterion
• Robust nonnegative matrix factorization via joint graph Laplacian and discriminative information for identifying differentially expressed genes
• Upper entropy of credal sets. Applications to credal classification
• Uncertainty and Information
• Uncertainty measures on probability intervals from the imprecise Dirichlet model
• Disaggregated total uncertainty measure for credal sets
• Quantum deformation of the Poincare supergroup and kappa -deformed superspace
• Random forests
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