caa a22 4500 605477930 CHVBK 20210128100403.0 cr unu---uuuuu 210128e20151001xx s 000 0 eng 10.1007/s10994-014-5452-1 doi (NATIONALLICENCE)springer-10.1007/s10994-014-5452-1 Two-level quantile regression forests for bias correction in range prediction [Elektronische Daten] [Thanh-Tung Nguyen, Joshua Huang, Thuy Nguyen] Quantile regression forests (QRF), a tree-based ensemble method for estimation of conditional quantiles, has been proven to perform well in terms of prediction accuracy, 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 second level 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. The Author(s), 2014 Bias correction nationallicence Random forests nationallicence Quantile regression forests nationallicence High dimensional data nationallicence Data mining nationallicence Nguyen Thanh-Tung Shenzhen Key Laboratory of High Performance Data Mining, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, 518055, Shenzhen, China aut Huang Joshua Shenzhen Key Laboratory of High Performance Data Mining, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, 518055, Shenzhen, China aut Nguyen Thuy Faculty of Information Technology, Vietnam National University of Agriculture, Hanoi, Vietnam aut Machine Learning Springer US; http://www.springer-ny.com 101/1-3(2015-10-01), 325-343 0885-6125 101:1-3<325 2015 101 10994 https://doi.org/10.1007/s10994-014-5452-1 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s10994-014-5452-1 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Nguyen Thanh-Tung Shenzhen Key Laboratory of High Performance Data Mining, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, 518055, Shenzhen, China aut NATIONALLICENCE 700 1- Huang Joshua Shenzhen Key Laboratory of High Performance Data Mining, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, 518055, Shenzhen, China aut NATIONALLICENCE 700 1- Nguyen Thuy Faculty of Information Technology, Vietnam National University of Agriculture, Hanoi, Vietnam aut NATIONALLICENCE 773 0- Machine Learning Springer US; http://www.springer-ny.com 101/1-3(2015-10-01), 325-343 0885-6125 101:1-3<325 2015 101 10994