Predicting the Tear Strength of Woven Fabrics Via Automated Machine Learning: An Application of the CRISP-DM Methodology

Rui Ribeiro, André Pilastri, Carla Moura, Filipe Rodrigues, Rita Rocha, Paulo Cortez

2020

Abstract

Textile and clothing is an important industry that is currently being transformed by the adoption of the Industry 4.0 concept. In this paper, we use the CRoss-Industry Standard Process for Data Mining (CRISP-DM) methodology to model the textile testing process. Real-world data were collected from a Portuguese textile company. Predicting the outcome of a given textile test is beneficial to the company because it can reduce the number of physical samples that are needed to be produced when designing new fabrics. In particular, we target two important textile regression tasks: the tear strength in warp and weft directions. To better focus on feature engineering and data transformations, we adopt an Automated Machine Learning (AutoML) during the modeling stage of the CRISP-DM. Several iterations of the CRISP-DM methodology were employed, using different data preprocessing procedures (e.g., removal of outliers). The best predictive models were achieved after 2 (for warp) and 3 (for weft) CRISP-DM iterations.

Paper Citation

in Harvard Style

Ribeiro R., Pilastri A., Moura C., Rodrigues F., Rocha R. and Cortez P. (2020). Predicting the Tear Strength of Woven Fabrics Via Automated Machine Learning: An Application of the CRISP-DM Methodology.In Proceedings of the 22nd International Conference on Enterprise Information Systems - Volume 1: ICEIS, ISBN 978-989-758-423-7, pages 548-555. DOI: 10.5220/0009411205480555

in Bibtex Style

@conference{iceis20,
author={Rui Ribeiro and André Pilastri and Carla Moura and Filipe Rodrigues and Rita Rocha and Paulo Cortez},
title={Predicting the Tear Strength of Woven Fabrics Via Automated Machine Learning: An Application of the CRISP-DM Methodology},
booktitle={Proceedings of the 22nd International Conference on Enterprise Information Systems - Volume 1: ICEIS,},
year={2020},
pages={548-555},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009411205480555},
isbn={978-989-758-423-7},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 22nd International Conference on Enterprise Information Systems - Volume 1: ICEIS,
TI - Predicting the Tear Strength of Woven Fabrics Via Automated Machine Learning: An Application of the CRISP-DM Methodology
SN - 978-989-758-423-7
AU - Ribeiro R.
AU - Pilastri A.
AU - Moura C.
AU - Rodrigues F.
AU - Rocha R.
AU - Cortez P.
PY - 2020
SP - 548
EP - 555
DO - 10.5220/0009411205480555