Feature selection for the low industrial yield of cane sugar production based on rule learning algorithms

• Autorzy: Gil Rodríguez Yohan , Socorro Llanes Raisa , Rosete Alejandro , Bravo Ilisástigui Lisandra

Identyfikatory

DOI

10.14313/JAMRIS/1‐2023/2

• Języki publikacji: EN

Abstrakty

EN

This article presents a model based on machine learning for the selection of the characteristics that most influence the low industrial yield of cane sugar production in Cuba. The set of data used in this work corresponds to a period of ten years of sugar harvests from 2010 to 2019. A pro‐ cess of understanding the business and of understand‐ ing and preparing the data is carried out. The accuracy of six rule learning algorithms is evaluated: CONJUNC‐ TIVERULE, DECISIONTABLE, RIDOR, FURIA, PART and JRIP. The results obtained allow us to identify: R417, R379, R378, R419a, R410, R613, R1427 and R380, as the indi‐ cators that most influence low industrial performance.

Słowa kluczowe

EN

feature selection; rule learning; data mining; CRISP-DM; industrial yield

• Wydawca: Łukasiewicz Industrial Research Institute for Automation and Measurements PIAP
• Czasopismo: Journal of Automation Mobile Robotics and Intelligent Systems
• Rocznik: 2023
• Tom: Vol. 17, No. 1
• Strony: 13--21
• Opis fizyczny: Bibliogr. 34 poz., rys.

Twórcy

autor

Gil Rodríguez Yohan

• ESI DATAZUCAR, AZCUBA, Avenida 23 No.171/ N y O, Vedado, Plaza de la Revolución, La Habana, Cuba

• https://orcid.org/0000-0002-8239-4124

autor

Socorro Llanes Raisa

• CUJAE, Calle 114 No.11901/ Ciclovía y Rotonda, Marianao, La Habana, Cuba

• https://orcid.org/0000-0002-2627-1912

autor

Rosete Alejandro

• CUJAE, Calle 114 No.11901/ Ciclovía y Rotonda, Marianao, La Habana, Cuba

• https://orcid.org/0000-0002-4579-3556

autor

Bravo Ilisástigui Lisandra

• CUJAE, Calle 114 No.11901/ Ciclovía y Rotonda, Marianao, La Habana, Cuba

• https://orcid.org/0000-0002-8209-4121

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Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).