EROS: Ensemble rough subspaces

DOI10.1016/j.patcog.2007.04.022zbMath1129.68074OpenAlexW2021236931MaRDI QIDQ996469

J. Martínez

Publication date: 14 September 2007

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

Full work available at URL: https://doi.org/10.1016/j.patcog.2007.04.022

zbMATH Keywords

rough setattribute reductionmultiple classifier systemensemble learningselective ensemble

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Pattern recognition, speech recognition (68T10)

Related Items (6)

A random approximate reduct-based ensemble learning approach and its application in software defect prediction ⋮ Collective-agreement-based pruning of ensembles ⋮ Taxonomy for characterizing ensemble methods in classification tasks: a review and annotated bibliography ⋮ Selection-fusion approach for classification of datasets with missing values ⋮ Gaussian kernel based fuzzy rough sets: model, uncertainty measures and applications ⋮ Ensemble learning based on approximate reducts and bootstrap sampling

Uses Software

• UCI-ml
• AdaBoost.MH
• NeC4.5

Cites Work

• Unnamed Item
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• Ensembling neural networks: Many could be better than all
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• Boosting a weak learning algorithm by majority
• Improved boosting algorithms using confidence-rated predictions
• Using diversity of errors for selecting members of a committee classifier
• Automatic identification of music performers with learning ensembles
• Neural network ensembles: evaluation of aggregation algorithms
• Selected tree classifier combination based on both accuracy and error diversity
• Rough set methods in feature selection and recognition
• Rough reduction in algebra view and information view
• Random forests
• Reduction algorithms based on discernibility matrix: The ordered attributes method

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