<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>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 process of understanding the business and of understanding and preparing the data is carried out. The accuracy of six rule learning algorithms is evaluated: CONJUNCTIVERULE, DECISIONTABLE, RIDOR, FURIA, PART and JRIP. The results obtained allow us to identify: R417, R379, R378, R419a, R410, R613, R1427 and R380, as the indicators that most influence low industrial performance.</p>
</abstract>

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 process of understanding the business and of understanding and preparing the data is carried out. The accuracy of six rule learning algorithms is evaluated: CONJUNCTIVERULE, DECISIONTABLE, RIDOR, FURIA, PART and JRIP. The results obtained allow us to identify: R417, R379, R378, R419a, R410, R613, R1427 and R380, as the indicators that most influence low industrial performance.

Feature Selection for the Low Industrial Yield of Cane Sugar Production Based on Rule Learning Algorithms

Journal of Automation, Mobile Robotics and Intelligent Systems

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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...

Feature Selection for the Low Industrial Yield of Cane Sugar Production Based on Rule Learning Algorithms

Online veröffentlicht: 26. Dez. 2023

Seitenbereich: 13 - 21

Eingereicht: 10. Aug. 2022

Akzeptiert: 19. Okt. 2022

DOI: https://doi.org/10.14313/jamris/1-2023/2

Schlüsselwörter
Feature selection, Rule learning, Data mining, CRISP-DM, Industrial yield

© 2023 Yohan Gil Rodríguez et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Feature Selection for the Low Industrial Yield of Cane Sugar Production Based on Rule Learning Algorithms

Online veröffentlicht: 26. Dez. 2023

Seitenbereich: 13 - 21

Eingereicht: 10. Aug. 2022

Akzeptiert: 19. Okt. 2022

DOI: https://doi.org/10.14313/jamris/1-2023/2

SchlüsselwörterFeature selection, Rule learning, Data mining, CRISP-DM, Industrial yield

© 2023 Yohan Gil Rodríguez et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Schlüsselwörter
Feature selection, Rule learning, Data mining, CRISP-DM, Industrial yield