中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
Two Level Quantile Regression Forests for Bias Correction in Range Prediction

文献类型:期刊论文

作者Nguyen, Thanh-Tung; Huang, Joshua Z.; Nguyen, Thuy Th
刊名Machine Learning
出版日期2014
英文摘要Quantile regression forests (QRF), a tree-based ensemble method for estimation of conditional quantiles, has been proven to perform well in terms of predictionaccuracy, especially for range prediction. However, the model may have bias and suffer from working with high dimensional data (thousands of features). In this paper, we propose a new bias correction method, called bcQRF that uses bias correction in QRF for range prediction. In bcQRF, a new feature weighting subspace sampling method is used to build the first level QRF model. The residual term of the first level QRF model is then used as the response feature to train the secondlevel QRF model for bias correction. The two-level models are used to compute bias-corrected predictions. Extensive experiments on both synthetic and real world data sets have demonstrated that the bcQRF method significantly reduced prediction errors and outperformed most existing regression random forests. The new method performed especially well on high dimensional data. © 2014 The Author(s).(22 refs)
收录类别EI
原文出处http://link.springer.com/article/10.1007/s10994-014-5452-1
语种英语
源URL[http://ir.siat.ac.cn:8080/handle/172644/5965]  
专题深圳先进技术研究院_数字所
作者单位Machine Learning
推荐引用方式
GB/T 7714
Nguyen, Thanh-Tung,Huang, Joshua Z.,Nguyen, Thuy Th. Two Level Quantile Regression Forests for Bias Correction in Range Prediction[J]. Machine Learning,2014.
APA Nguyen, Thanh-Tung,Huang, Joshua Z.,&Nguyen, Thuy Th.(2014).Two Level Quantile Regression Forests for Bias Correction in Range Prediction.Machine Learning.
MLA Nguyen, Thanh-Tung,et al."Two Level Quantile Regression Forests for Bias Correction in Range Prediction".Machine Learning (2014).

入库方式: OAI收割

来源:深圳先进技术研究院

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