Using Heterogeneous Model Ensembles to Improve the Prediction of Yeast Contamination in Peppermint

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@Article{ANLAUF:2022:procs,

• author = "Stefan Anlauf and Andreas Haghofer and Karl Dirnberger and Stephan M. Winkler",
• title = "Using Heterogeneous Model Ensembles to Improve the Prediction of Yeast Contamination in Peppermint",
• journal = "Procedia Computer Science",
• volume = "200",
• pages = "1194--1200",
• year = "2022",
• note = "3rd International Conference on Industry 4.0 and Smart Manufacturing",
• ISSN = "1877-0509",
• DOI = "doi:10.1016/j.procs.2022.01.319",
• URL = "https://www.sciencedirect.com/science/article/pii/S1877050922003283",
• keywords = "genetic algorithms, genetic programming, yeast contamination, herbs, machine learning, heterogeneous model ensembles",
• abstract = "In this paper, we present an heterogeneous ensemble modeling approach to learn predictors for yeast contamination in freshly harvested peppermint batches. Our research is based on data about numerous parameters of the harvesting process, such as planting, tillage, fertilization, harvesting, drying, as well as information about microbial contamination. We use several different machine learning methods, namely random forests, gradient boosting trees, symbolic regression by genetic programming, and support vector machines to learn models that predict contamination on the basis of available harvesting parameters. Using those models we form model ensembles in order to improve the accuracy as well as to reduce the false negative rate, i.e., to oversee as few contaminations as possible. As we summarize in this paper, ensemble modeling indeed helps to increase the prediction accuracy for our application, especially when using only the best models. The final prediction accuracy as well as other statistical indicators such as false negative rate and false positive rate depend on the choice of the discrimination threshold; in the optimal case, model ensembles are able to predict yeast contamination with 65.91percent accuracy and only 19.15percent of the samples are false negative, i.e., overseen contaminations",

}

Genetic Programming entries for Stefan Anlauf Andreas Haghofer Karl Dirnberger Stephan M Winkler

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