Random Forest using tree selection method to classify unbalanced data
文献类型：会议论文
| 作者 | Baoxun Xu; Yunming Ye; Qiang Wang; Junjie Li; Xiaojun Chen |
| 出版日期 | 2012 |
| 会议名称 | 4th International Conference on Digital Image Processing (ICDIP) |
| 会议地点 | 马来西亚 |
| 英文摘要 | Random forest is a popular classification algorithm used to build ensemble models of decision tree classifiers. However, owing to the complexity of unbalanced data distribution in high dimensional space, a random forest may include bad trees that can result in wrong results. This paper proposed an improved random forest algorithm with tree selection methods. This algorithm is particularly designed for analyzing unbalanced data. The novel tree selection methods are developed for making random forest framework well suited to classify unbalanced data. Experimental results on unbalanced datasets with diverse characteristics have demonstrated that the proposed method could generate a random forest model with higher performance than the random forests generated by Breiman's method.© (2012) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only. |
| 收录类别 | EI |
| 语种 | 英语 |
| 源URL | [http://ir.siat.ac.cn:8080/handle/172644/4228]  |
| 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,Yunming Ye,Qiang Wang,et al. Random Forest using tree selection method to classify unbalanced data[C]. 见:4th International Conference on Digital Image Processing (ICDIP). 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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