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dc.contributor.authorKhasawneh, Natheer
dc.contributor.authorConrad, Stefan
dc.contributor.authorFraiwan, Luay
dc.contributor.authorTaqieddin, Eyad
dc.contributor.authorKhasawneh, Basheer
dc.date.accessioned2018-03-19T08:37:44Z
dc.date.available2018-03-19T08:37:44Z
dc.date.issued2012
dc.identifier.urihttps://dspace.adu.ac.ae/handle/1/733
dc.descriptionKhasawneh, N., Conrad, S., Fraiwan, L., Taqieddin, E., & Khasawneh, B. (2012). Combining decision trees classifiers: a case study of automatic sleep stage scoring.
dc.description.abstractThis paper presents a new approach of classification in which multiple decision trees are combined together for achieving better accuracy compared to that achieved by each of the individual constituent decision trees. A major unit of the proposed system is the combination unit for which we present two algorithms; one is based on pre-pruning and true positive rate and the other is based on maximum probability voting. In presenting this new method, we use the case study of sleep stage scoring as a basis of demonstration. For such a task, two tree classifications are utilised. We performed a tree classification based on the training data and then combined the resulting model with another classification tree supplemented by the expert according to Rechtschaffen and Kale’s sleep scoring rules. We applied this method to nine recordings, six of which were used to construct the training tree and the remaining three were used for testing. The experiments showed that the combination method has a 7% better accuracy over a single model.en_US
dc.language.isoenen_US
dc.publisherInderscienceonlineen_US
dc.subjectCombining Classifiersen_US
dc.subjectDecision Treesen_US
dc.subjectSleep Scoringen_US
dc.subjectknowledge Engineeringen_US
dc.subjectData Miningen_US
dc.titleCombining decision trees classifiers: a case study of automatic sleep stage scoringen_US
dc.typeArticleen_US
dc.identifier.doihttps://doi.org/10.1504/IJKEDM.2012.044707


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