Advanced deep graph neural networks for explainable anomaly detection study

用于可解释异常检测研究的高级深度图神经网络

• 批准号: 22K17961
• 负责人: Ouyang Tinghui
• 金额: $ 2.41万
• 依托单位: National Institute of Advanced Industrial Science and Technology
• 依托单位国家: 日本
• 项目类别: Grant-in-Aid for Early-Career Scientists
• 财政年份: 2022
• 资助国家: 日本
• 起止时间: 2022-04-01 至 2025-03-31
• 项目状态: 未结题
• 来源: https://kaken.nii.ac.jp/en/grant/KAKENHI-PROJECT-22K17961/
• 关键词: granular computing; data mining; anomaly data detection; information graph; structure description; explanation; graph neural network; anomaly detection; explainability; big data analysis

In this fiscal year, there are totally three achievements achieved, including granular data description, granular ADD algorithms and its application.The first achievement is about granular data description, we have developed advanced granular computing for data description, aiming to reduce the computation cost of big data analysis. Especially, structural granules are constructed for data distribution description and data structure mining in this task.The second one is about anomaly data detection algorithms. By making use of the constructed granules, especially ones having the ability of describing data structure, a primary anomaly data detection method via rule-based modeling method was built. Its performance was evaluated good on both supervised scenarios with noise and unsupervised scenarios.The third achievement is about completing some industrial applications related to anomaly data detection. Through these applications, it is verified useful by considering data structure information in anomaly data detection.

在这个财政年度，总共取得了三项成就，包括颗粒状数据描述，颗粒状添加算法及其应用。第一个成就是关于粒状数据描述的，我们已经开发了高级粒状计算来进行数据描述，旨在降低计算成本大数据分析。特别是，在此任务中构建了用于数据分布描述和数据结构挖掘的结构颗粒。第二个是关于异常数据检测算法的。通过使用构造的颗粒，尤其是具有描述数据结构能力的颗粒，建立了基于规则的建模方法的主要异常数据检测方法。在两种有噪音和无监督的方案的监督场景中，都对其性能进行了良好的评估。第三个成就是完成与异常数据检测有关的一些工业应用。通过这些应用程序，通过考虑异常数据检测中的数据结构信息来验证它。

项目成果

Extension of DBSCAN in Online Clustering: An Approach Based on Three-Layer Granular Models

• DOI: 10.3390/app12199402
• 发表时间: 2022-09
• 期刊: Applied Sciences
• 作者: Xinhui Zhang;Xun Shen;Tinghui Ouyang

DBSCAN-based granular descriptors for rule-based modeling

• DOI: 10.1007/s00500-022-07514-w
• 发表时间: 2022-09
• 期刊: Soft Computing
• 影响因子: 4.1
• 作者: Tinghui Ouyang;Xinhui Zhang

Online structural clustering based on DBSCAN extension with granular descriptors

• DOI: 10.1016/j.ins.2022.06.027
• 发表时间: 2022-06
• 期刊: Inf. Sci.
• 作者: Tinghui Ouyang;Xun Shen

Granular Description of Uncertain Data for Classification Rules in Three-Way Decision

三支决策中分类规则的不确定数据的粒度描述

• DOI: 10.3390/app122211381
• 发表时间: 2022
• 期刊: Applied Sciences
• 作者: Zhang Xinhui;Ouyang Tinghui
• 通讯作者: Ouyang Tinghui

Fuzzy rule-based anomaly detectors construction via information granulation

• DOI: 10.1016/j.ins.2022.12.011
• 发表时间: 2022-12
• 期刊: Inf. Sci.
• 作者: Tinghui Ouyang;Xinhui Zhang