A dynamic overproduce-and-choose strategy for the selection of classifier ensembles

DOI10.1016/j.patcog.2008.03.027zbMath1151.68602OpenAlexW2042053856MaRDI QIDQ936409

Eulanda M. Dos Santos, Patrick Maupin, Robert Sabourin

Publication date: 13 August 2008

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

Full work available at URL: https://doi.org/10.1016/j.patcog.2008.03.027

zbMATH Keywords

optimizationdynamic classifier selectionmeasures of confidenceoverproduce-and-choose strategy

Mathematics Subject Classification ID

Pattern recognition, speech recognition (68T10)

Related Items (6)

Distant diversity in dynamic class prediction ⋮ Cost-sensitive business failure prediction when misclassification costs are uncertain: a heterogeneous ensemble selection approach ⋮ Fuzzy decision fusion approach for loss-given-default modeling ⋮ A probabilistic model of classifier competence for dynamic ensemble selection ⋮ MSEBAG: a dynamic classifier ensemble generation based on ‘minimum-sufficient ensemble' and bagging ⋮ Evaluation of incremental learning algorithms for HMM in the recognition of alphanumeric characters

Cites Work

• Bagging predictors
• Measures of diversity in classifier ensembles and their relationship with the ensemble accuracy
• Ensembling neural networks: Many could be better than all
• From dynamic classifier selection to dynamic ensemble selection
• Selected tree classifier combination based on both accuracy and error diversity
• A study on the performances of dynamic classifier selection based on local accuracy estimation
• A MULTIPLE CLASSIFIER SYSTEM USING AMBIGUITY REJECTION FOR CLUSTERING-CLASSIFICATION COOPERATION
• CP PHASES IN THE LEPTONIC SECTOR OF LRSUSY AND LEPTOGENESIS
• Dynamic classifier selection based on multiple classifier behaviour
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