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Using a classifier ensemble for proactive quality monitoring and control: The impact of the choice of classifiers types, selection criterion, and fusion process.

, , , , , and . Comput. Ind., (2018)

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CART for Supply Chain Simulation Models Reduction - Application to a Sawmill Internal Supply Chain., , and . APMS (3), volume 440 of IFIP Advances in Information and Communication Technology, page 530-537. Springer, (2014)Neural Networks Ensemble for Quality Monitoring., , , , and . IJCCI, page 515-522. SciTePress, (2013)A New Multilayer Perceptron Pruning Algorithm for Classification and Regression Applications., and . Neural Processing Letters, 42 (2): 437-458 (2015)Relearning procedure to adapt pollutant prediction neural model: Choice of relearning algorithm., , and . IJCNN, page 1-8. IEEE, (2019)Neural Network Inverse Model for Quality Monitoring - Application to a High Quality Lackering Process., , , , , and . IJCCI, page 186-191. SciTePress, (2017)Variance Sensitivity Analysis of Parameters for Pruning of a Multilayer Perceptron: Application to a Sawmill Supply Chain Simulation Model., , and . Adv. Artificial Neural Systems, (2013)Impact of Hidden Weights Choice on Accuracy of MLP with Randomly Fixed Hidden Neurons for Regression Problems., and . IJCCI, page 223-230. SciTePress, (2017)Formalisation of a new prognosis model for supporting proactive maintenance implementation on industrial system., , and . Reliab. Eng. Syst. Saf., 93 (2): 234-253 (2008)Using a classifier ensemble for proactive quality monitoring and control: The impact of the choice of classifiers types, selection criterion, and fusion process., , , , , and . Comput. Ind., (2018)Modélisation de processus industriels par réseaux bayésiens orientés objet (RBOO)., and . Rev. d'Intelligence Artif., 18 (2): 299-326 (2004)