Ensemble Learning based on Regressor Chains: A Case on Quality Prediction
dc.contributor.author | Demirel, Kenan Cem | |
dc.contributor.author | Şahin, Ahmet | |
dc.contributor.author | Albey, Erinç | |
dc.date.accessioned | 2024-03-12T12:33:23Z | |
dc.date.available | 2024-03-12T12:33:23Z | |
dc.date.issued | 2019 | |
dc.identifier.isbn | 978-989758377-3 | |
dc.identifier.uri | http://hdl.handle.net/10679/9294 | |
dc.identifier.uri | https://www.scitepress.org/Link.aspx?doi=10.5220/0007932802670274 | |
dc.description.abstract | In 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 reserved | en_US |
dc.language.iso | eng | en_US |
dc.publisher | SciTePress | en_US |
dc.relation.ispartof | DATA 2019 - Proceedings of the 8th International Conference on Data Science, Technology and Applications | |
dc.rights | restrictedAccess | |
dc.title | Ensemble Learning based on Regressor Chains: A Case on Quality Prediction | en_US |
dc.type | Conference paper | en_US |
dc.contributor.department | Özyeğin University | |
dc.contributor.authorID | (ORCID 0000-0001-5004-0578 & YÖK ID 144710) Albey, Erinç | |
dc.contributor.ozuauthor | Albey, Erinç | |
dc.identifier.startpage | 267 | en_US |
dc.identifier.endpage | 274 | en_US |
dc.identifier.wos | WOS:000570730200030 | |
dc.identifier.doi | 10.5220/0007932802670274 | en_US |
dc.subject.keywords | Ensemble methods | en_US |
dc.subject.keywords | Industry 4.0 | en_US |
dc.subject.keywords | Multi-target regression | en_US |
dc.subject.keywords | Quality prediction | en_US |
dc.subject.keywords | Regression chains | en_US |
dc.subject.keywords | Textile manufacturing | en_US |
dc.identifier.scopus | SCOPUS:2-s2.0-85072958872 | |
dc.contributor.ozugradstudent | Demirel, Kenan Cem | |
dc.contributor.ozugradstudent | Şahin, Ahmet | |
dc.relation.publicationcategory | Conference Paper - International - Institutional Academic Staff, Graduate Student and PhD Student |
Files in this item
Files | Size | Format | View |
---|---|---|---|
There are no files associated with this item. |
This item appears in the following Collection(s)
Share this page