Event log-based fraud rating using interval type-2 fuzzy sets in fuzzy AHP

2016 IEEE Region 10 Conference (TENCON) Pub Date : 2016-11-01 DOI:10.1109/TENCON.2016.7848366

Evi Septiana Pane, A. Wibawa, M. Purnomo

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引用次数: 9

Abstract

Fraud on the event logs derived from business process is known as event log-based fraud. Fraud detection using event logs employs process mining technique, notably conformance checking analysis. This study proposes a method for rating event log-based fraud datasets using fuzzy analytic hierarchy process (AHP), a well-known multicriteria decision-making method. Earlier studies proposed that interval type-2 fuzzy set provides an alternative in handling uncertainty than type-1 fuzzy set. Therefore, we utilize interval type-2 fuzzy sets in fuzzy AHP in order to manage vagueness in many linguistic judgement. This study includes linguistic hedges implementation to modify the membership function of expert valuation. The experimental results showed comparable types of membership function shape and its obtained accuracy performance. Obtained accuracy from fuzzy type-1 AHP method achieved 94% for triangular membership function shape and 93.9% for interval type-2 fuzzy AHP. Although there was no escalation in accuracy after applying interval-valued fuzzy sets for all scenarios, the rank of fraud weight in each feature were altered.

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模糊层次分析法中基于事件日志的区间2型模糊集欺诈评级

对源自业务流程的事件日志的欺诈称为基于事件日志的欺诈。使用事件日志的欺诈检测采用流程挖掘技术，特别是一致性检查分析。本文提出了一种基于事件日志的欺诈数据集评级方法，该方法采用了一种著名的多准则决策方法——模糊层次分析法(AHP)。较早的研究提出区间2型模糊集比1型模糊集在处理不确定性方面提供了另一种选择。因此，我们在模糊层次分析法中利用区间2型模糊集来管理许多语言判断中的模糊性。本研究包括运用语言模糊限制来修正专家评价的隶属函数。实验结果表明，所得到的隶属函数形状和精度性能均具有可比性。模糊1型AHP方法对三角隶属函数形状的准确率达到94%，对区间2型模糊AHP的准确率达到93.9%。虽然在对所有场景应用区间值模糊集后，准确率没有上升，但每个特征中的欺诈权重等级被改变了。

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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2016 IEEE Region 10 Conference (TENCON)

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