通过变阈值寻找贝叶斯和Naïve贝叶斯模型在欺诈公司分类中的最佳性能

A. Widodo, S. Handoyo, G. Irianto, N. W. Hidajati, Dewi Sri Susanti, I. Purwanto
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引用次数: 1

摘要

-欺诈检测是防止个人和组织欺诈的第一步。开发一种高性能的分类模型来检测欺诈是机器学习建模中一个有趣的话题。寻找最好的贝叶斯和朴素贝叶斯分类模型是一个至关重要的问题,因为这两种模型都是简单且易于应用于生命科学和社会科学领域的模型。本研究旨在获得基于概率概念开发的分类模型的最佳性能,即贝叶斯和朴素贝叶斯模型。在这两个模型的决策标准中加入一个阈值是一种期望能够找到性能更好的模型的努力。对欺诈公司的审计数据包括775家来自澳大利亚不同商业部门的公司作为案例研究。测试数据由100个实例组成,采用整群随机抽样的方法,其中非欺诈企业61家,欺诈企业39家,其余实例作为训练数据。最好的贝叶斯模型在0.22的阈值下平均准确率为84%。而最好的朴素贝叶斯模型在15个阈值中获得的平均准确率为94%。加入阈值对贝叶斯模型的性能有显著的影响,可以将平均准确率从36%提高到84%。另一方面,朴素贝叶斯模型的平均准确率仅提高了1%,从93%提高到94%。朴素贝叶斯模型的敏感性、特异性、F1评分、ROC曲线等性能指标也优于贝叶斯模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Finding the Best Performance of Bayesian and Naïve Bayes Models in Fraudulent Firms Classification through Varying Threshold
—Fraud detection is the first step to preventing fraud committed by both individuals and organizations. The development of a high-performance classification model to detect fraud is an interesting topic in machine learning modeling. A finding of the best Bayesian and Naive Bayes classification models is a crucial issue because both models are simple and easily applied models in the fields of life and social sciences. This study aims to obtain the best performance of classification models developed based on probability concepts, namely Bayesian and Naive Bayes models. Adding a threshold value to the decision-making criteria of the two models is an effort expected to can find models that perform superiorly. Data on the auditing of fraudulent firms containing of 775 firms from various business sectors in Australia is used as a case study. The testing data consisting of 100 instances were taken by cluster random sampling with a proportion of 61 non-fraudulent and 39 fraudulent firms and the remaining instances as the training data. The best Bayesian model has an average accuracy of 84% obtained at a threshold value of 0.22. While the best Naive Bayes model has an average accuracy of 94% which is obtained in the 15 threshold values. Adding the threshold value has a significant impact on the performance of the Bayesian model, which can increase the average accuracy from 36% to 84%. On the other hand, the average accuracy of the Naive Bayes model only increased by 1%, from 93% to 94%. Performance measures Sensitivity, Specificity, F1 score, and ROC curve of the Naive Bayes model are also superior to the Bayesian model.
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