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COMSIS
2006

A Comparison of the Bagging and the Boosting Methods Using the Decision Trees Classifiers

8 years 11 months ago
A Comparison of the Bagging and the Boosting Methods Using the Decision Trees Classifiers
In this paper we present an improvement of the precision of classification algorithm results. Two various approaches are known: bagging and boosting. This paper describes a set of experiments with bagging and boosting methods. Our use of these methods aims at classification algorithms generating decision trees. Results of performance tests focused on the use of the bagging and boosting methods in connection with binary decision trees are presented. The minimum number of decision trees, which enables an improvement of the classification performed by the bagging and boosting methods, was found. The tests were carried out using the Reuter's 21578 collection of documents as well as documents from an Internet portal of TV broadcasting company Mark
Kristína Machova, Miroslav Puszta, Frantise
Added 11 Dec 2010
Updated 11 Dec 2010
Type Journal
Year 2006
Where COMSIS
Authors Kristína Machova, Miroslav Puszta, Frantisek Barcák, Peter Bednár
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