期刊
CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS
卷 144, 期 -, 页码 56-62出版社
ELSEVIER SCIENCE BV
DOI: 10.1016/j.chemolab.2015.03.008
关键词
Consensus modeling; Error correlation; Partial least squares; Interval partial least squares; Consensus interval partial least squares; Near infrared spectra
类别
资金
- National Natural Science Foundation of China [61174161, 61473329, 61375077, 61201358, 31201355]
- Natural Science Foundation of Fujian Province of China [2012J01154]
- specialized Research Fund for the Doctoral Program of Higher Education of China [20130121130004, 20120121120038]
- Fundamental Research Funds for the Central Universities in China [2013121025, 201412G009, CBX2014038]
- Natural Sciences and Engineering Research Council of Canada
This paper proposes a novel consensus modeling method for regression, which optimizes the weight coefficients of member models considering both error and error correlation of member models. Thus, the optimized objective function has clear physical significance. Furthermore, the root-mean-square error of cross-validation (RMSECV) and root-mean-square error of prediction (RMSEP) of the consensus model are better than any member model. Integrating this method with interval partial least squares algorithm (iPLS), the novel consensus interval partial least squares algorithm (CPLS) is achieved. The typical near infrared spectroscopy datasets are used to validate the effectiveness of CPLS. Compared to the commonly used partial least squares (PLS), iPLS and staked interval partial least squares algorithm (SPLS), CPLS produces better prediction performance. (C) 2015 Elsevier B.V. All rights reserved.
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