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.

在本财年，我们共取得了三项成果，包括粒度数据描述、粒度ADD算法及其应用。第一项成果是关于粒度数据描述，我们开发了高级粒度计算用于数据描述，旨在降低大数据分析的计算成本。特别地，本文还构造了结构粒，用于数据分布描述和数据结构挖掘。利用构造的粒，特别是具有描述数据结构能力的粒，建立了基于规则建模的原始异常数据检测方法。实验结果表明，该算法在有噪声的监督场景和无监督场景下都有较好的检测效果。第三个成果是完成了一些与异常数据检测相关的工业应用。通过这些应用，验证了在异常数据检测中考虑数据结构信息的有效性。

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

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

Structural rule-based modeling with granular computing

• DOI: 10.1016/j.asoc.2022.109519
• 发表时间: 2022-08
• 期刊: Appl. Soft Comput.
• 作者: Tinghui Ouyang

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