Detecting fuzzy-rough conditional anomalies

IF 8.1 1区计算机科学 0 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information Sciences Pub Date : 2024-10-28 DOI:10.1016/j.ins.2024.121560

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引用次数: 0

Abstract

The purpose of conditional anomaly detection is to identify samples that significantly deviate from the majority of other samples under specific conditions within a dataset. It has been successfully applied to numerous practical scenarios such as forest fire prevention, gas well leakage detection, and remote sensing data analysis. Aiming at the issue of conditional anomaly detection, this paper utilizes the characteristics of fuzzy rough set theory to construct a conditional anomaly detection method that can effectively handle numerical or mixed attribute data. By defining the fuzzy inner boundary, the subset of contextual data is first divided into two parts, i.e. the fuzzy lower approximation and the fuzzy inner boundary. Subsequently, the fuzzy inner boundary is further divided into two distinct segments: the fuzzy abnormal boundary and the fuzzy main boundary. So far, three-way regions can be obtained, i.e., the fuzzy abnormal boundary, the fuzzy main boundary, and the fuzzy lower approximation. Then, a fuzzy-rough conditional anomaly detection model is constructed based on the above three-way regions. Finally, a related algorithm is proposed for the detection model and its effectiveness is verified by data experiments.

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检测模糊粗糙条件异常

条件异常检测的目的是在数据集的特定条件下，识别出与其他大多数样本有明显偏差的样本。它已成功应用于森林防火、气井泄漏检测和遥感数据分析等众多实际场景。针对条件异常检测问题，本文利用模糊粗糙集理论的特点，构建了一种能有效处理数值或混合属性数据的条件异常检测方法。通过定义模糊内边界，首先将上下文数据子集分为两部分，即模糊下近似和模糊内边界。随后，模糊内边界被进一步划分为两个不同的部分：模糊异常边界和模糊主边界。至此，可以得到三个方向的区域，即模糊异常边界、模糊主边界和模糊下近似边界。然后，基于上述三向区域构建了模糊粗糙条件异常检测模型。最后，针对该检测模型提出了相关算法，并通过数据实验验证了其有效性。

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

Information Sciences 工程技术-计算机：信息系统

CiteScore

14.00

自引率

17.30%

发文量

1322

审稿时长

10.4 months

期刊介绍： Informatics and Computer Science Intelligent Systems Applications is an esteemed international journal that focuses on publishing original and creative research findings in the field of information sciences. We also feature a limited number of timely tutorial and surveying contributions. Our journal aims to cater to a diverse audience, including researchers, developers, managers, strategic planners, graduate students, and anyone interested in staying up-to-date with cutting-edge research in information science, knowledge engineering, and intelligent systems. While readers are expected to share a common interest in information science, they come from varying backgrounds such as engineering, mathematics, statistics, physics, computer science, cell biology, molecular biology, management science, cognitive science, neurobiology, behavioral sciences, and biochemistry.