Data- & compute-efficient deviance mining via active learning and fast ensembles

IF 2.3 3区计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Journal of Intelligent Information Systems Pub Date : 2024-01-23 DOI:10.1007/s10844-024-00841-4

引用次数: 0

Abstract

Detecting deviant traces in business process logs is crucial for modern organizations, given the harmful impact of deviant behaviours (e.g., attacks or faults). However, training a Deviance Prediction Model (DPM) by solely using supervised learning methods is impractical in scenarios where only few examples are labelled. To address this challenge, we propose an Active-Learning-based approach that leverages multiple DPMs and a temporal ensembling method that can train and merge them in a few training epochs. Our method needs expert supervision only for a few unlabelled traces exhibiting high prediction uncertainty. Tests on real data (of either complete or ongoing process instances) confirm the effectiveness of the proposed approach.

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通过主动学习和快速集合进行数据和计算效率较高的偏差挖掘

摘要鉴于异常行为（如攻击或故障）的有害影响，检测业务流程日志中的异常痕迹对现代组织至关重要。然而，在只有少数示例被标记的情况下，仅使用监督学习方法来训练偏差预测模型（DPM）是不切实际的。为了应对这一挑战，我们提出了一种基于主动学习的方法，该方法利用多个 DPM 和一种时间集合方法，可以在几个训练历时内训练和合并这些 DPM。我们的方法只需要专家的监督，就能对少数表现出高度预测不确定性的未标记轨迹进行预测。对真实数据（完整或正在进行的流程实例）的测试证实了所提方法的有效性。

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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来源期刊

Journal of Intelligent Information Systems 工程技术-计算机：人工智能

CiteScore

7.20

自引率

11.80%

发文量

审稿时长

6-12 weeks

期刊介绍： The mission of the Journal of Intelligent Information Systems: Integrating Artifical Intelligence and Database Technologies is to foster and present research and development results focused on the integration of artificial intelligence and database technologies to create next generation information systems - Intelligent Information Systems. These new information systems embody knowledge that allows them to exhibit intelligent behavior, cooperate with users and other systems in problem solving, discovery, access, retrieval and manipulation of a wide variety of multimedia data and knowledge, and reason under uncertainty. Increasingly, knowledge-directed inference processes are being used to: discover knowledge from large data collections, provide cooperative support to users in complex query formulation and refinement, access, retrieve, store and manage large collections of multimedia data and knowledge, integrate information from multiple heterogeneous data and knowledge sources, and reason about information under uncertain conditions. Multimedia and hypermedia information systems now operate on a global scale over the Internet, and new tools and techniques are needed to manage these dynamic and evolving information spaces. The Journal of Intelligent Information Systems provides a forum wherein academics, researchers and practitioners may publish high-quality, original and state-of-the-art papers describing theoretical aspects, systems architectures, analysis and design tools and techniques, and implementation experiences in intelligent information systems. The categories of papers published by JIIS include: research papers, invited papters, meetings, workshop and conference annoucements and reports, survey and tutorial articles, and book reviews. Short articles describing open problems or their solutions are also welcome.