Author
Demirel, Kenan Cem, Şahin, Ahmet, Albey, Erinç
Publication Date
2019
Publication Place
-
SciTePress
Subject
Ensemble methods, Industry 4.0, Multi-target regression, Quality prediction, Regression chains, Textile manufacturing
Type
Document
Language
English
Digital
Yes
Manuscript
No
Library
Özyeğin University
Library Asset ID
978-989758377-3
Record ID
1546d4fb-26be-4d59-a48a-3fe630fa73c1
Library Location
Industrial Engineering
Date
2019
Sample Text
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
DOI
10.5220/0007932802670274
