Demirel, Kenan CemŞahin, AhmetAlbey, Erinç2024-03-122024-03-122019978-989758377-3http://hdl.handle.net/10679/9294https://doi.org/10.5220/0007932802670274In this study we construct a prediction model, which utilizes the production process parameters acquired from a textile machine and predicts the quality characteristics of the final yarn. Several machine learning algorithms (decision tree, multivariate adaptive regression splines and random forest) are used for prediction. An ensemble method, using the idea of regressor chains, is developed to further improve the prediction performance. Collected data is first segmented into two parts (labeled as “normal” and “unusual”) using local outlier factor method, and performance of the algorithms are tested for each segment separately. It is seen that ensemble idea proves its competence especially for the cases where the collected data is categorized as unusual. In such cases ensemble algorithm improves the prediction accuracy significantly. Copyright © 2019 by SCITEPRESS – Science and Technology Publications, Lda. All rights reservedenginfo:eu-repo/semantics/restrictedAccessConference paper26727400057073020003010.5220/0007932802670274Ensemble methodsIndustry 4.0Multi-target regressionQuality predictionRegression chainsTextile manufacturing2-s2.0-85072958872