TY - GEN
T1 - Hybrid random forests
T2 - 16th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2012
AU - Xu, Baoxun
AU - Huang, Joshua Zhexue
AU - Williams, Graham
AU - Li, Mark Junjie
AU - Ye, Yunming
PY - 2012
Y1 - 2012
N2 - Random forests are a popular classification method based on an ensemble of a single type of decision tree. In the literature, there are many different types of decision tree algorithms, including C4.5, CART and CHAID. Each type of decision tree algorithms may capture different information and structures. In this paper, we propose a novel random forest algorithm, called a hybrid random forest. We ensemble multiple types of decision trees into a random forest, and exploit diversity of the trees to enhance the resulting model. We conducted a series of experiments on six text classification datasets to compare our method with traditional random forest methods and some other text categorization methods. The results show that our method consistently outperforms these compared methods.
AB - Random forests are a popular classification method based on an ensemble of a single type of decision tree. In the literature, there are many different types of decision tree algorithms, including C4.5, CART and CHAID. Each type of decision tree algorithms may capture different information and structures. In this paper, we propose a novel random forest algorithm, called a hybrid random forest. We ensemble multiple types of decision trees into a random forest, and exploit diversity of the trees to enhance the resulting model. We conducted a series of experiments on six text classification datasets to compare our method with traditional random forest methods and some other text categorization methods. The results show that our method consistently outperforms these compared methods.
KW - Classification
KW - Decision Tree
KW - Hybrid Random Forest
KW - Random Forests
UR - http://www.scopus.com/inward/record.url?scp=84861442725&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-30217-6_13
DO - 10.1007/978-3-642-30217-6_13
M3 - Conference contribution
AN - SCOPUS:84861442725
SN - 9783642302169
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 147
EP - 158
BT - Advances in Knowledge Discovery and Data Mining - 16th Pacific-Asia Conference, PAKDD 2012, Proceedings
Y2 - 29 May 2012 through 1 June 2012
ER -