一个新的多目标灰狼优化模糊矿工用于过程发现:将稳定性和可解释性纳入模型评估中.
Mohammad Salehi1, Rauof Khayami1, Mirpouya Mirmozaffari2
1Computer Engineering and Information Technology Department, Shiraz University of Technology, Shiraz, Iran.
PloS one
|March 4, 2026
概括
模糊多目标灰狼优化 (Fuzzy MOGWO) 通过优化六个指标来增强过程发现,包括噪声弹性和可解释性. 这种新的方法在无噪音和噪音环境中明显优于现有方法.
科学领域:
- 过程采矿的过程采矿.
- 人工智能的人工智能是人工智能.
- 超启发式优化优化方法
背景情况:
- 过程挖掘分析事件日志以了解和改进业务流程.
- 现有的方法往往在噪音上扎,缺乏可解释性.
- 需要强大且可解释的过程发现技术.
研究的目的:
- 介绍用于过程发现的模糊多目标灰狼优化 (模糊MOGWO).
- 同时优化六个关键指标:适应性,精确性,概括性,简单性,稳定性和可解释性.
- 评估Fuzzy MOGWO的性能与已建立的过程挖掘算法相比.
主要方法:
- 模糊建模与多标准的元启发式优化方法的整合.
- 开发一种使用L2标准进行平衡客观评价的规范化评分机制.
- 在合成和现实世界的事件日志上比较Fuzzy MOGWO与Alpha Miner,Inductive Miner和Fuzzy Miner,包括杂的数据集.
主要成果:
- 在无噪声条件下,Fuzzy MOGWO实现了0.329的正常化得分,比最好的基线高出14.24%.
- 在杂的环境中,Fuzzy MOGWO得分为0.440,超过了顶级竞争对手的16.40%.
- 在现实世界日志上,Fuzzy MOGWO在6个指标中的4个中超过了竞争对手,证明了卓越的有效性和稳定性.
结论:
- 的MOGWO提供了一个全面和可靠的解决方案,用于具有挑战性的过程发现任务.
- 拟议的方法显著提高了有效性,在噪音下表现强,并提高了可解释性.
- 通过平衡多个关键性能维度,Fuzzy MOGWO为多目标过程发现设定了新的标准.
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