Feature Reduction and Selection for Use in Machine Learning for Manufacturing
Alrufaihi, Duaa ; Oleghe, Omogbai ; Almanei, Mohammed ; Jagtap, Sandeep LU
and Salonitis, Konstantinos
(2022)
19th International Conference on Manufacturing Research, ICMR 2022
In Advances in Transdisciplinary Engineering
25.
p.289-296
- Abstract
In a complex manufacturing system such as the multistage manufacturing system, maintaining the quality of the products becomes a challenging task. It is due to the interconnectivity and dependency of factors that can affect the final product. With the increasing availability of data, Machine Learning (ML) approaches are applied to assess and predict quality-related issues. In this paper, several ML algorithms, including feature reduction/selection methods, were applied to a publicly available multistage manufacturing dataset to predict the characteristic of the output measurements in (mm). A total of 24 prediction models were produced. The accuracy of the prediction models and the execution time were the evaluation metrics. The results... (More)
In a complex manufacturing system such as the multistage manufacturing system, maintaining the quality of the products becomes a challenging task. It is due to the interconnectivity and dependency of factors that can affect the final product. With the increasing availability of data, Machine Learning (ML) approaches are applied to assess and predict quality-related issues. In this paper, several ML algorithms, including feature reduction/selection methods, were applied to a publicly available multistage manufacturing dataset to predict the characteristic of the output measurements in (mm). A total of 24 prediction models were produced. The accuracy of the prediction models and the execution time were the evaluation metrics. The results show that uncontrolled variables are the most common features that have been selected by the selection/reduction methods suggesting their strong relationship to the quality of the product. The performance of the prediction models was heavily dependent on the ML algorithm.
(Less)
- author
- Alrufaihi, Duaa
; Oleghe, Omogbai
; Almanei, Mohammed
; Jagtap, Sandeep
LU
and Salonitis, Konstantinos
- publishing date
- 2022-11-08
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- keywords
- algorithms, Complex manufacturing systems, Machine Learning
- host publication
- Advances in Manufacturing Technology XXXV : Proceedings of the 19th International Conference on Manufacturing Research, Incorporating the 36th National Conference on Manufacturing Research - Proceedings of the 19th International Conference on Manufacturing Research, Incorporating the 36th National Conference on Manufacturing Research
- series title
- Advances in Transdisciplinary Engineering
- editor
- Shafik, Mahmoud and Case, Keith
- volume
- 25
- pages
- 8 pages
- publisher
- IOS Press
- conference name
- 19th International Conference on Manufacturing Research, ICMR 2022
- conference location
- Derby, United Kingdom
- conference dates
- 2022-09-06 - 2022-09-08
- external identifiers
-
- scopus:85145561617
- ISBN
- 9781614994398
- 9781643683300
- DOI
- 10.3233/ATDE220605
- language
- English
- LU publication?
- no
- additional info
- Publisher Copyright: © 2022 The authors and IOS Press.
- id
- 7025018a-6b11-4f12-8642-6b64de3dbf29
- date added to LUP
- 2023-09-17 15:59:34
- date last changed
- 2024-04-19 01:06:19
@inproceedings{7025018a-6b11-4f12-8642-6b64de3dbf29,
abstract = {{<p>In a complex manufacturing system such as the multistage manufacturing system, maintaining the quality of the products becomes a challenging task. It is due to the interconnectivity and dependency of factors that can affect the final product. With the increasing availability of data, Machine Learning (ML) approaches are applied to assess and predict quality-related issues. In this paper, several ML algorithms, including feature reduction/selection methods, were applied to a publicly available multistage manufacturing dataset to predict the characteristic of the output measurements in (mm). A total of 24 prediction models were produced. The accuracy of the prediction models and the execution time were the evaluation metrics. The results show that uncontrolled variables are the most common features that have been selected by the selection/reduction methods suggesting their strong relationship to the quality of the product. The performance of the prediction models was heavily dependent on the ML algorithm.</p>}},
author = {{Alrufaihi, Duaa and Oleghe, Omogbai and Almanei, Mohammed and Jagtap, Sandeep and Salonitis, Konstantinos}},
booktitle = {{Advances in Manufacturing Technology XXXV : Proceedings of the 19th International Conference on Manufacturing Research, Incorporating the 36th National Conference on Manufacturing Research}},
editor = {{Shafik, Mahmoud and Case, Keith}},
isbn = {{9781614994398}},
keywords = {{algorithms; Complex manufacturing systems; Machine Learning}},
language = {{eng}},
month = {{11}},
pages = {{289--296}},
publisher = {{IOS Press}},
series = {{Advances in Transdisciplinary Engineering}},
title = {{Feature Reduction and Selection for Use in Machine Learning for Manufacturing}},
url = {{http://dx.doi.org/10.3233/ATDE220605}},
doi = {{10.3233/ATDE220605}},
volume = {{25}},
year = {{2022}},
}