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dc.contributor.authorWoloszynski, Tomasz
dc.contributor.authorKurzynski, M.
dc.date.accessioned2017-01-30T13:59:43Z
dc.date.available2017-01-30T13:59:43Z
dc.date.created2016-09-12T08:36:40Z
dc.date.issued2010
dc.identifier.citationWoloszynski, T. and Kurzynski, M. 2010. A measure of competence based on randomized reference classifier for dynamic ensemble selection, pp. 4194-4197.
dc.identifier.urihttp://hdl.handle.net/20.500.11937/37118
dc.identifier.doi10.1109/ICPR.2010.1019
dc.description.abstract

This paper presents a measure of competence based on a randomized reference classifier (RRC) for classifier ensembles. The RRC can be used to model, in terms of class supports, any classifier in the ensemble. The competence of a modelled classifier is calculated as the probability of correct classification of the respective RRC. A multiple classifier system (MCS) was developed and its performance was compared against five MCSs using eight databases taken from the UCI Machine Learning Repository. The system developed achieved the highest overall classification accuracies for both homogeneous and heterogeneous ensembles. © 2010 IEEE.

dc.titleA measure of competence based on randomized reference classifier for dynamic ensemble selection
dc.typeConference Paper
dcterms.source.startPage4194
dcterms.source.endPage4197
dcterms.source.titleProceedings - International Conference on Pattern Recognition
dcterms.source.seriesProceedings - International Conference on Pattern Recognition
dcterms.source.isbn9780769541099
curtin.departmentDepartment of Mechanical Engineering
curtin.accessStatusFulltext not available


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