基于规则学习算法的蔗糖生产低工业产量特征选择

Q4 Engineering

Journal of Automation, Mobile Robotics and Intelligent Systems Pub Date : 2023-12-01 DOI:10.14313/jamris-1-2023-2

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

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引用次数: 0

摘要

摘要本文介绍了一个基于机器学习的模型，用于选择对古巴甘蔗制糖工业产量低影响最大的特征。这项工作中使用的数据集与 2010 年至 2019 年的十年蔗糖收成相对应。对业务进行了了解，并对数据进行了理解和准备。对六种规则学习算法的准确性进行了评估：这六种算法是：CONJUNCTIVERULE、DECISIONTABLE、RIDOR、FURIA、PART 和 JRIP。根据评估结果，我们可以确定R417、R379、R378、R419a、R410、R613、R1427 和 R380 是对低工业绩效影响最大的指标。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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

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.

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来源期刊

Journal of Automation, Mobile Robotics and Intelligent Systems Engineering-Control and Systems Engineering

CiteScore

1.10

自引率

0.00%

发文量

期刊介绍： Fundamentals of automation and robotics Applied automatics Mobile robots control Distributed systems Navigation Mechatronics systems in robotics Sensors and actuators Data transmission Biomechatronics Mobile computing