A generalized adaptive ensemble generation and aggregation approach for multiple classifier systems

DOI10.1016/J.PATCOG.2008.09.003zbMath1178.68486OpenAlexW2034018567MaRDI QIDQ1010069

Lei Chen, Mohamed S. Kamel

Publication date: 3 April 2009

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

Full work available at URL: https://doi.org/10.1016/j.patcog.2008.09.003

zbMATH Keywords

classificationpattern recognitiondata miningmultiple classifier systemsclassifier combination

Mathematics Subject Classification ID

Pattern recognition, speech recognition (68T10)

Related Items (1)

MSEBAG: a dynamic classifier ensemble generation based on ‘minimum-sufficient ensemble' and bagging

Uses Software

• UCI-ml
• ElemStatLearn

Cites Work

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• Decision-level fusion in fingerprint verification
• Use of Dempster-Shafer theory to combine classifiers which use different class boundaries
• Classification by evolutionary ensembles
• Using diversity of errors for selecting members of a committee classifier
• Classifier combination based on confidence transformation
• Adaptive fusion and co-operative training for classifier ensembles
• Combining Pattern Classifiers
• The elements of statistical learning. Data mining, inference, and prediction
• Dynamic classifier selection based on multiple classifier behaviour
• Decision templates for multiple classifier fusion: An experimental comparison

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