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- Title
Two-level quantile regression forests for bias correction in range prediction.
- Authors
Nguyen, Thuy; Nguyen, Thanh-Tung; Huang, Joshua
- Abstract
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.
- Subjects
BIAS correction (Topology); QUANTILE regression; RANDOM forest algorithms; STATISTICAL bias; CORRECTION factors; DATA mining; PREVENTION
- Publication
Machine Learning, 2015, Vol 101, Issue 1-3, p325
- ISSN
0885-6125
- Publication type
Article
- DOI
10.1007/s10994-014-5452-1