Bias-Corrected Quantile Regression Forests for High-Dimensional Data
文献类型：会议论文
| 作者 | Tungl, Nguyen Thanh; Huangl, Joshua Zhexue; Nguyen, Thuy Thi; Khanl, Imran |
| 出版日期 | 2014 |
| 会议名称 | 13th International Conference on Machine Learning and Cybernetics, ICMLC 2014 |
| 会议地点 | Lanzhou |
| 英文摘要 | The Quantile Regression Forest (QRF), a nonparametric regression method based on the random forests, has been proved to perform well in terms of prediction accuracy, especially for nonGaussian conditional distributions. However, the method may have two kinds of bias when solving regression problems: bias in the feature selection stage and bias in solving the regression problem. In this paper, we propose a new bias-correction algorithm that uses bias correction based on the QRF. To correct the first kind of bias, we propose a new scheme for feature sampling that allows to select good features for growing trees. The first level QRF is built based on this. For the second kind of bias, the residual term of the first level QRF model is used as the response feature to train the second level QRF model forbias correction. The second level model is then used to compute bias-corrected predictions. In our experiments, the proposedalgorithm dramatically reduces prediction errors and outperforms most of the existing regression random forests models for both synthetic and well-known real-world data sets. © 2014 IEEE.(14 refs) |
| 收录类别 | EI |
| 语种 | 英语 |
| 源URL | [http://ir.siat.ac.cn:8080/handle/172644/6039]  |
| 专题 | 深圳先进技术研究院_数字所 |
| 作者单位 | 2014 |
| 推荐引用方式 GB/T 7714 | Tungl, Nguyen Thanh,Huangl, Joshua Zhexue,Nguyen, Thuy Thi,et al. Bias-Corrected Quantile Regression Forests for High-Dimensional Data[C]. 见:13th International Conference on Machine Learning and Cybernetics, ICMLC 2014. Lanzhou. |
入库方式： OAI收割
来源：深圳先进技术研究院
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