DocumentCode
1632279
Title
Experiments with Boosted Decision Tree Classifiers
Author
Wozniak, Michal
Author_Institution
Dept. of Syst. & Comput. Networks, Wroclaw Univ. of Technol., Wroclaw
Volume
1
fYear
2008
Firstpage
552
Lastpage
557
Abstract
Boosting is the most popular method of improving quality and stabilizing weak classifiers. It bases on the voting by the group of classifiers, where each of them is generated on the basis of modified original learning set. The modification of AdaBoost.M1 and experimental results of boosted C4.5 (decision tree induction) algorithm are presented. All experimental researches are made on well known benchmark databases.
Keywords
decision trees; learning (artificial intelligence); pattern classification; AdaBoost.M1; boosted decision tree classifiers; decision tree induction algorithm; Application software; Boosting; Classification tree analysis; Computer networks; Decision making; Decision trees; Induction generators; Intelligent networks; Intelligent systems; Voting; AdaBoost; boosting; decision tree;
fLanguage
English
Publisher
ieee
Conference_Titel
Intelligent Systems Design and Applications, 2008. ISDA '08. Eighth International Conference on
Conference_Location
Kaohsiung
Print_ISBN
978-0-7695-3382-7
Type
conf
DOI
10.1109/ISDA.2008.215
Filename
4696266
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