Ensemble Learning based on Regressor Chains: A Case on Quality Prediction

Name: Ensemble Learning based on Regressor Chains: A Case on Quality Prediction
Author: Demirel, Kenan Cem, Şahin, Ahmet, Albey, Erinç

İsim	Ensemble Learning based on Regressor Chains: A Case on Quality Prediction
Yazar	Demirel, Kenan Cem, Şahin, Ahmet, Albey, Erinç
Basım Tarihi:	2019
Basım Yeri	- SciTePress
Konu	Ensemble methods, Industry 4.0, Multi-target regression, Quality prediction, Regression chains, Textile manufacturing
Tür	Belge
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	978-989758377-3
Kayıt Numarası	1546d4fb-26be-4d59-a48a-3fe630fa73c1
Lokasyon	Industrial Engineering
Tarih	2019
Örnek Metin	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

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Ensemble Learning based on Regressor Chains: A Case on Quality Prediction

Yazar Demirel, Kenan Cem, Şahin, Ahmet, Albey, Erinç

Basım Tarihi 2019

Basım Yeri - SciTePress

Konu Ensemble methods, Industry 4.0, Multi-target regression, Quality prediction, Regression chains, Textile manufacturing

Tür Belge

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 978-989758377-3

Kayıt Numarası 1546d4fb-26be-4d59-a48a-3fe630fa73c1

Lokasyon Industrial Engineering

Tarih 2019

Örnek Metin 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