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

DOI10.1080/00207721.2015.1074762zbMath1333.62163OpenAlexW1848549407MaRDI QIDQ2795143

Mohamed S. Kamel, Lei Chen

Publication date: 18 March 2016

Published in: International Journal of Systems Science (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/00207721.2015.1074762

zbMATH Keywords

classificationpattern recognitiondata miningmultiple classifier systemsclassifier combination

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Pattern recognition, speech recognition (68T10)

Uses Software

• UCI-ml

Cites Work

• Sparse ensembles using weighted combination methods based on linear programming
• A family of measures for best top-\(n\) class-selective decision rules
• A dynamic overproduce-and-choose strategy for the selection of classifier ensembles
• A generalized adaptive ensemble generation and aggregation approach for multiple classifier systems
• Data dependency in multiple classifier systems
• Using diversity of errors for selecting members of a committee classifier
• From dynamic classifier selection to dynamic ensemble selection
• Classifier combination based on confidence transformation
• Hinging hyperplanes for regression, classification, and function approximation
• An ensemble ofk-nearest neighbours algorithm for detection of Parkinson's disease
• Adaptive Hypergraph Learning and its Application in Image Classification
• Online breakage detection of multitooth tools using classifier ensembles for imbalanced data
• Dynamic classifier selection based on multiple classifier behaviour
• Decision templates for multiple classifier fusion: An experimental comparison

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