Rahman Rahayu Abdul, Masrom Suraya, Mohamad Masurah, Sari Eka Nurmala, Saragih Fitriani, Rahman Abdullah Sani Abd
Faculty of Accountancy, Univesiti Teknologi MARA, Perak Branch, Tapah Campus, Malaysia.
College of Computing, Informatics and Mathematics, Universiti Teknologi MARA, Perak Branch, Tapah Campus, Malaysia.
MethodsX. 2023 Sep 7;11:102364. doi: 10.1016/j.mex.2023.102364. eCollection 2023 Dec.
Machine learning has been very promising in solving real problems, but the implementation involved difficulties mainly for the inexpert data scientists. Therefore, this paper presents an automated machine learning (AutoML) to simplify and accelerate the modeling tasks. Focused on Python and RapidMiner rapid modeling tools, Tree-based Pipeline Optimization Tool (TPOT) and AutoModel were used. This paper presents a comprehensive comparison between these tools with regard to the prediction accuracy and Area Under Curve (AUC) in classifying real cases of whistleblowing academic dishonesty among undergraduate students of two universities in Indonesia. Additionally, the correlations weight from demographic and Theory of Planned Behavior (TOB) attributes in the different machine learning models are also discussed. All the machine learning algorithms from TPOT and AutoModel are considerable powerful to generate good accuracy level (between 70-93% of AUC) in classifying both cases of whistleblowing and non-whistleblowing on the hold-out samples from the testing process. Generally, based on the validation results of the prediction models, demographic attributes presented more importance than the TBP attributes. The findings of this study will be a great interest of many research scholars to conduct a more in-depth analysis on AutoML for many domains mainly in education and academic misconduct fields.•AutoML is the first of its kind to be empirically compared between TPOT and AutoModel in an application to predict academic dishonesty whistleblowing.•Besides accuracy performances of the AutoML, the proportion of the variance of each attribute from demographic and Theory of Planned Behavior (TPB) is also presented in the prediction models of academic dishonesty whistleblowing.•AutoML is a convenient and reproducible rapid modeling method of machine learning to be used in many kinds of prediction problem.
机器学习在解决实际问题方面前景广阔，但对于非专业数据科学家而言，其实施过程存在诸多困难。因此，本文提出了一种自动化机器学习（AutoML）方法，以简化和加速建模任务。本文聚焦于Python和RapidMiner快速建模工具，使用了基于树的管道优化工具（TPOT）和自动模型（AutoModel）。本文对这两种工具在印度尼西亚两所大学的本科生学术不诚信举报实际案例分类中的预测准确性和曲线下面积（AUC）进行了全面比较。此外，还讨论了不同机器学习模型中人口统计学和计划行为理论（TOB）属性的相关权重。TPOT和AutoModel中的所有机器学习算法在对测试过程中的留出样本进行举报和非举报案例分类时，都具有相当强大的能力，能够产生较高的准确率水平（AUC在70%-93%之间）。总体而言，基于预测模型的验证结果，人口统计学属性比TBP属性更为重要。本研究的结果将引起许多研究学者的极大兴趣，促使他们对主要在教育和学术不端领域的许多领域的AutoML进行更深入的分析。
•AutoML是首次在预测学术不诚信举报的应用中对TPOT和AutoModel进行实证比较。
•除了AutoML的准确性表现外，学术不诚信举报预测模型中还呈现了人口统计学和计划行为理论（TPB）各属性方差的比例。
•AutoML是一种方便且可重复的机器学习快速建模方法，可用于多种预测问题。
