Hybrid random forests: advantages of mixed trees in classifying text data

Authors:
Baoxun Xu;Joshua Zhexue Huang;Graham Williams;Mark Junjie Li;Yunming Ye
Affiliations:
Department of Computer Science, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, China;Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China;Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China;Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China;Department of Computer Science, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, China
Venue:
PAKDD'12 Proceedings of the 16th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
Year:
2012

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Abstract

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.