An improvement of the classification algorithm results

dc.contributor.authorMachová, Kristína
dc.contributor.authorPuszta, Miroslav
dc.contributor.authorBednár, Peter
dc.date.accessioned2024-09-04T09:45:22Z
dc.date.available2024-09-04T09:45:22Z
dc.date.issued2006-06-01
dc.description.abstractOne of the most important aspects of the precision of a classification is the suitable selection of a classification algorithm and a training set for a given task. Basic principles of machine learning can be used for this selection [3]. In this paper, we have focused on improving the precision of classification algorithms results. Two kinds of approaches are known: Boosting and Bagging. This paper describes the use of the first method – boosting [6] which aims at algorithms generating decision trees. A modification of the AdaBoost algorithm was implemented. Another similar method called Bagging [1] is mentioned. Results of performance tests focused on the use of the boosting method on binary decision trees are presented. The minimum number of decision trees, which enables improvement of the classification performed by a base machine learning algorithm, was found. The tests were carried out using the Reuters 21578 collection of documents and documents from an internet portal of TV Markíza.en
dc.formatapplication/pdf
dc.identifier.citationTeaching Mathematics and Computer Science, Vol. 4 No. 1 (2006) , 131-142
dc.identifier.doihttps://doi.org/10.5485/TMCS.2006.0109
dc.identifier.eissn2676-8364
dc.identifier.issn1589-7389
dc.identifier.issue1
dc.identifier.jatitleTeach. Math. Comp. Sci.
dc.identifier.jtitleTeaching Mathematics and Computer Science
dc.identifier.urihttps://hdl.handle.net/2437/379573en
dc.identifier.volume4
dc.languageen
dc.relationhttps://ojs.lib.unideb.hu/tmcs/article/view/14763
dc.rights.accessOpen Access
dc.rights.ownerKristína Machová, Miroslav Puszta and Peter Bednár
dc.subjectclassification algorithmsen
dc.subjectboostingen
dc.subjectbinary decision treesen
dc.subjecttext categorizationen
dc.titleAn improvement of the classification algorithm resultsen
dc.typefolyóiratcikkhu
dc.typearticleen
dc.type.detailedidegen nyelvű folyóiratközlemény hazai lapbanhu
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