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

Kenan Demirel, Ahmet Şahin, Erinc Albey

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.

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Paper Citation


in Harvard Style

Demirel K., Şahin A. and Albey E. (2019). Ensemble Learning based on Regressor Chains: A Case on Quality Prediction.In Proceedings of the 8th International Conference on Data Science, Technology and Applications - Volume 1: DATA, ISBN 978-989-758-377-3, pages 267-274. DOI: 10.5220/0007932802670274


in Bibtex Style

@conference{data19,
author={Kenan Demirel and Ahmet Şahin and Erinc Albey},
title={Ensemble Learning based on Regressor Chains: A Case on Quality Prediction},
booktitle={Proceedings of the 8th International Conference on Data Science, Technology and Applications - Volume 1: DATA,},
year={2019},
pages={267-274},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007932802670274},
isbn={978-989-758-377-3},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 8th International Conference on Data Science, Technology and Applications - Volume 1: DATA,
TI - Ensemble Learning based on Regressor Chains: A Case on Quality Prediction
SN - 978-989-758-377-3
AU - Demirel K.
AU - Şahin A.
AU - Albey E.
PY - 2019
SP - 267
EP - 274
DO - 10.5220/0007932802670274