中国科学院深圳先进技术研究院机构知识库(SIAT OpenIR): Hybrid random forests: Advantages of mixed trees in classifying text data
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Hybrid random forests: Advantages of mixed trees in classifying text data
Baoxun Xu; Joshua Zhexue Huang; Graham Williams; Mark Junjie Li; Yunming Ye
2012
Conference Name16TH Pacific-Asia Conference, PAKDD 2012
Conference Place马来西亚
AbstractRandom 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.
Department高性能计算技术研究中心
Indexed ByEI
Language英语
Document Type会议论文
Identifierhttp://ir.siat.ac.cn/handle/172644/4227
Collection数字所
Affiliation2012
Recommended Citation
GB/T 7714
Baoxun Xu,Joshua Zhexue Huang,Graham Williams,et al. Hybrid random forests: Advantages of mixed trees in classifying text data[C],2012.
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