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RoBoost-PLS2-R: An extension of RoBoost-PLSR method for multi-response

Metz M., Ryckewaert M., Mas-Garcia S., Bendoula R., Dardenne P., Lesnoff M., Roger J.M.. 2022. Chemometrics and Intelligent Laboratory Systems, 222 : 9 p..

DOI: 10.1016/j.chemolab.2022.104498

Recently, a novel robust PLSR method was developed to address the problem of outliers in the data. In this paper, an extension of this method, called RoBoost-PLS2-R is proposed to predict multi-response variables. Robustness and efficiency of this new approach have been validated on two simulated data sets and one real data set containing different outlier scenarios. Its performance was also compared with reference methods (PLS2-R and RSIMPLS) for predicting multi-response variables. Results confirm that RoBoost-PLS2-R greatly reduces prediction errors when data contain outliers. Prediction performances of RoBoost-PLS2-R are close to the optimal model (PLS2-R) calibrated without outliers and also to RSIMPLS method. This method seems to be a reliable and a competitive robust regression tool for predicting multi-response variables.

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