An improved ensemble partial least squares for analysis of near-infrared spectra

Hu, Yong; Peng, Silong; Peng, Jiangtao; Wei, Jiping

doi:10.1016/j.talanta.2012.03.047

Cited by 18 publications

(12 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…So Zhou et al suggested that it may be a better choice for using part of sub-models instead of all of the sub-models [41]. Herein, original training set is arbitrarily divided into tress parts: calibration sets, validation sets and prediction sets [15]. We establish 100 PLS sub-models in the validation sets by sub-sampling and re-weighting the existed calibration samples respectively.…”

Section: The Sub-models Selective Rulementioning

confidence: 99%

Principal Model Analysis Based on Partial Least Squares

Xie¹,

Tang²,

Li³

et al. 2019

Preprint

Self Cite

View full text Add to dashboard Cite

Motivated by the Bagging Partial Least Squares (PLS) and Principal Component Analysis (PCA) algorithms, we propose a Principal Model Analysis (PMA) method in this paper. In the proposed PMA algorithm, the PCA and the PLS are combined. In the method, multiple PLS models are trained on sub-training sets, derived from the original training set based on the random sampling with replacement method. The regression coefficients of all the sub-PLS models are fused in a joint regression coefficient matrix. The final projection direction is then estimated by performing the PCA on the joint regression coefficient matrix. The proposed PMA method is compared with other traditional dimension reduction methods, such as PLS, Bagging PLS, Linear discriminant analysis (LDA) and PLS-LDA. Experimental results on six public datasets show that our proposed method can achieve better classification performance and is usually more stable.

show abstract

Section: The Sub-models Selective Rulementioning

confidence: 99%

Principal Model Analysis Based on Partial Least Squares

Xie¹,

Tang²,

Li³

et al. 2019

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Another strategy is to produce virtual samples to expand the scope of the original samples. The ensemble model is built based on the virtual and real samples . A more complicated strategy is to generate training subsets, which is a multilevel approach.…”

Section: Ensemble Calibration Strategymentioning

confidence: 99%

Ensemble calibration for the spectral quantitative analysis of complex samples

Bian

Diwu

Liu

et al. 2017

Journal of Chemometrics

View full text Add to dashboard Cite

Ensemble strategies have gained increasing attention in multivariate calibration for quantitative analysis of complex samples. The aim of ensemble calibration is to obtain a more accurate, stable, and robust prediction by combining the predictions of multiple submodels. The generation and calibration of the training subsets, as well as the integration of the submodels, are three keys to the success of ensemble calibration. Many training subset generating and submodel integrating strategies have been developed to form numerous ensemble calibration methods for improving the performance of the basic calibration method. This contribution focuses on the recent ensemble strategies in relation to calibration, especially the ensemble modeling for quantitative analysis of complex samples. The limitations and perspectives of ensemble strategies are also discussed.

show abstract

“…Bagging, random forest and boosting are three famous ensemble methods, which were attractive for scientists from various fields over the last decades . These methods were originally developed to generalize the inherent abilities of a single decision tree and overcome its two drawbacks: instability and low accuracy.…”

Section: Theorymentioning

confidence: 99%

An improved ensemble learning machine for biological activity prediction of tyrosine kinase inhibitors

Tavakoli

Ghasemi

2015

Journal of Chemometrics

View full text Add to dashboard Cite

Boosting is one of the most important strategies in ensemble learning because of its ability to improve the stability and performance of weak learners. It is nonparametric, multivariate, fast and interpretable but is not robust against outliers. To enhance its prediction accuracy as well as immunize it against outliers, a modified version of a boosting algorithm (AdaBoost R2) was developed and called AdaBoost R3. In the sampling step, extremum samples were added to the boosting set. In the robustness step, a modified Huber loss function was applied to overcome the outlier problem. In the output step, a deterministic threshold was used to guarantee that bad predictions do not participate in the final output. The performance of the modified algorithm was investigated with two anticancer data sets of tyrosine kinase inhibitors, and the mechanism of inhibition was studied using the relative weighted variable importance procedure. Investigating the effect of base learner's strength reveals that boosting is only successful using the classification and regression tree method (a weak to moderate learner) and does not have a significant effect using the radial basis functions partial least square method (a strong base learners).

show abstract

An improved ensemble partial least squares for analysis of near-infrared spectra

Cited by 18 publications

References 16 publications

Principal Model Analysis Based on Partial Least Squares

Principal Model Analysis Based on Partial Least Squares

Ensemble calibration for the spectral quantitative analysis of complex samples

An improved ensemble learning machine for biological activity prediction of tyrosine kinase inhibitors

Contact Info

Product

Resources

About