Abstract
Process Monitoring for Quality is a manufacturing quality philosophy aimed at defect detection through binary classification that is founded on big data and big models. Genetic Programming (GP) algorithms have been successfully applied by following the big models learning paradigm for rare quality event detection (classification). Since it is a bias-free technique unmarred by human preconceptions, it can potentially generate better solutions (models) compared with the best human efforts. However, since GP uses random search methods based on Darwinian philosophy of “survival of the fittest”, hundreds, or even thousands of models need to be created to find a good solution. In this context, model selection becomes a critical step in the process of finding the final model to be deployed at the plant. A three-objective optimization model selection criterion (\(3D-GP\)) is introduced for analyzing highly/ultra unbalanced data structures. It uses three competing attributes – prediction, separability, complexity – to project candidate models into a three-dimensional space to select the final model that solves the posed tradeoff between them the best.
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Escobar, C.A., Wegner, D.M., Gaur, A., Morales-Menendez, R. (2019). Process-Monitoring-for-Quality—A Model Selection Criterion for Genetic Programming. In: Deb, K., et al. Evolutionary Multi-Criterion Optimization. EMO 2019. Lecture Notes in Computer Science(), vol 11411. Springer, Cham. https://doi.org/10.1007/978-3-030-12598-1_13
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