Hybrid random forests: Advantages of mixed trees in classifying text data
文献类型：会议论文
| 作者 | Baoxun Xu; Joshua Zhexue Huang; Graham Williams; Mark Junjie Li; Yunming Ye |
| 出版日期 | 2012 |
| 会议名称 | 16TH Pacific-Asia Conference, PAKDD 2012 |
| 会议地点 | 马来西亚 |
| 英文摘要 | 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. |
| 收录类别 | EI |
| 语种 | 英语 |
| 源URL | [http://ir.siat.ac.cn:8080/handle/172644/4227]  |
| 专题 | 深圳先进技术研究院_数字所 |
| 作者单位 | 2012 |
| 推荐引用方式 GB/T 7714 | Baoxun Xu,Joshua Zhexue Huang,Graham Williams,et al. Hybrid random forests: Advantages of mixed trees in classifying text data[C]. 见:16TH Pacific-Asia Conference, PAKDD 2012. 马来西亚. |
入库方式： OAI收割
来源：深圳先进技术研究院
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