Online active learning of decision trees with evidential data

DOI10.1016/j.patcog.2015.10.014zbMath1394.68302OpenAlexW2197872593MaRDI QIDQ1669781

Yong Wang, Liyao Ma, Sebastien Destercke

Publication date: 4 September 2018

Published in: Pattern Recognition (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.patcog.2015.10.014

zbMATH Keywords

uncertain datadecision treeactive learningbelief functionsevidential likelihood

Mathematics Subject Classification ID

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

Related Items (6)

Parametric classification with soft labels using the evidential EM algorithm: linear discriminant analysis versus logistic regression ⋮ Bim-afa: Belief information measure-based attribute fusion approach in improving the quality of uncertain data ⋮ Joint Feature Transformation and Selection Based on Dempster-Shafer Theory ⋮ Rough set-based feature selection for weakly labeled data ⋮ Decision tree classifiers for evidential attribute values and class labels ⋮ Racing trees to query partial data

Uses Software

• UCI-ml
• C4.5

Cites Work

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• Handling missing values with regularized iterative multiple correspondence analysis
• Pruning belief decision tree methods in averaging and conjunctive approaches
• Harnessing the information contained in low-quality data sources
• Learning from imprecise and fuzzy observations: data disambiguation through generalized loss minimization
• Likelihood-based belief function: justification and some extensions to low-quality data
• Active Learning
• On the Foundations of Statistical Inference
• Learning Theory and Kernel Machines
• Upper and Lower Probabilities Induced by a Multivalued Mapping
• Belief decision trees: Theoretical foundations

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