DocumentCode
2023415
Title
Boosting simple decision trees with Bayesian learning for text categorization
Author
Diao, LiLi ; Hu, Keyun ; Lu, Yuchang ; Shi, Chunyi
Author_Institution
Dept. of Comput. Sci. & Technol., Tsinghua Univ., Beijing, China
Volume
1
fYear
2002
fDate
2002
Firstpage
321
Abstract
Introduces a Bayesian method to select best base classifiers for a boosting algorithm that is used for solving text categorization problems. This method is specifically shaped for an improved version of AdaBoost.MH, an effective multi-class multi-label text classification algorithm. The paper also proposes a method to facilitate its convergence. Experimental results show that these changes improve not only the accuracy, but also the efficiency of boosting algorithms for text categorization.
Keywords
Bayes methods; convergence; decision trees; learning (artificial intelligence); text analysis; Bayesian learning; best base classifiers; boosting algorithm; convergence; simple decision trees; text categorization; Bayesian methods; Boosting; Classification algorithms; Classification tree analysis; Computer science; Convergence; Decision trees; Intelligent systems; Laboratories; Text categorization;
fLanguage
English
Publisher
ieee
Conference_Titel
Intelligent Control and Automation, 2002. Proceedings of the 4th World Congress on
Print_ISBN
0-7803-7268-9
Type
conf
DOI
10.1109/WCICA.2002.1022121
Filename
1022121
Link To Document