A Machine Learning Decision Support Framework for Industrial Engineering Purposes

Anlie Du Preez, James Bekker
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引用次数: 1

Abstract

Data is currently one of the most critical and influential emerging assets. However, the true potential of data is yet to be exploited since, currently, about 1% of generated data is ever actually analyzed for value creation [1]. There is a data gap where data is not explored due to the lack of data analytics infrastructure and the required data analytics skills. This study developed a decision support framework for data analytics by following Jabareen's framework development methodology. The study focused on machine learning algorithms, which is a subset of data analytics. The developed framework is designed to assist data analysts with little experience, in choosing the appropriate machine learning algorithm given the purpose of their application.
面向工业工程的机器学习决策支持框架
数据是当前最关键、最具影响力的新兴资产之一。然而,数据的真正潜力尚未得到开发,因为目前,大约1%的生成数据被用于实际分析以创造价值。由于缺乏数据分析基础设施和所需的数据分析技能,存在数据缺口,即没有对数据进行探索。本研究通过遵循Jabareen的框架开发方法,为数据分析开发了一个决策支持框架。这项研究的重点是机器学习算法,这是数据分析的一个子集。开发的框架旨在帮助经验不足的数据分析师根据其应用目的选择适当的机器学习算法。
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