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dc.contributor.authorWoloszynski, Tomasz
dc.contributor.authorKurzynski, M.
dc.date.accessioned2017-01-30T13:53:09Z
dc.date.available2017-01-30T13:53:09Z
dc.date.created2016-09-12T08:36:40Z
dc.date.issued2008
dc.identifier.citationWoloszynski, T. and Kurzynski, M. 2008. Combining classifiers in a tree structure, pp. 785-790.
dc.identifier.urihttp://hdl.handle.net/20.500.11937/36035
dc.identifier.doi10.1109/CIMCA.2008.22
dc.description.abstract

This paper presents a method for combining classifiers in a tree structure, where each node of the tree contains single hypothesis trained in respective region of the feature space. All base classifiers are then combined using weighted average. Majority vote and Newton-Raphson numerical optimization are used for fitting the coefficients in the additive model. Two loss functions (quadratic and boosting-like exponential) as well as new splitting criteria for inducing the tree are examined within proposed framework. The idea of combining classifiers in a tree structure is then compared with other iteratively built classifiers: Adaboost.MH and MART (multiple additive regression trees). The experiments were conducted with the usage of well-known databases from the UCI Repository and the ELENA project. © 2008 IEEE.

dc.titleCombining classifiers in a tree structure
dc.typeConference Paper
dcterms.source.startPage785
dcterms.source.endPage790
dcterms.source.title2008 International Conference on Computational Intelligence for Modelling Control and Automation, CIMCA 2008
dcterms.source.series2008 International Conference on Computational Intelligence for Modelling Control and Automation, CIMCA 2008
dcterms.source.isbn9780769535142
curtin.departmentDepartment of Mechanical Engineering
curtin.accessStatusFulltext not available


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