Journal
CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS
Volume 105, Issue 1, Pages 1-6Publisher
ELSEVIER SCIENCE BV
DOI: 10.1016/j.chemolab.2010.10.004
Keywords
Bootstrap aggregating; Ensemble modelling; Near infrared spectroscopy; Non-linear calibration; Robust model
Ask authors/readers for more resources
This paper presents the application of the bagging technique for non-linear regression models to obtain more accurate and robust calibration of spectroscopy. Bagging refers to the combination of multiple models obtained by bootstrap re-sampling with replacement into an ensemble model to reduce prediction errors. It is well suited to non-robust models, such as the non-linear calibration methods of artificial neural network (ANN) and Gaussian process regression (GPR), in which small changes in data or model parameters can result in significant change in model predictions. A specific variant of bagging, based on sub-sampling without replacement and named subagging, is also investigated, since it has been reported to possess similar prediction capability to bagging but requires less computation. However, this work shows that the calibration performance of subagging is sensitive to the amount of sub-sampled data, which needs to be determined by computationally intensive cross-validation. Therefore, we suggest that bagging is preferred to subagging in practice. Application study on two near infrared datasets demonstrates the effectiveness of the presented approach. (c) 2010 Elsevier B.V. All rights reserved.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available