Fuzzy rule-based anomaly detectors construction via information granulation
Fuzzy rule-based anomaly detectors construction via information granulation
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DOI：
10.1016/j.ins.2022.12.011
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发表时间：
2022-12
期刊：
影响因子：
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通讯作者：
Tinghui Ouyang;Xinhui Zhang
中科院分区：
文献类型：
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作者：
Tinghui Ouyang;Xinhui Zhang
从给定的原始数据集中检测和去除异常数据对于数据驱动的研究是有意义的。针对现有异常数据检测（ADD）方法存在异常描述的可信度和不确定性、ADD中核学习和深度学习的计算量大等问题，提出了一种构造低成本有效ADD检测器的改进方法。首先，利用信息粒良好的数据描述能力，构造了用于异常和正常数据描述的粒数据描述符。然后，基于这些数据描述符，基于重构的策略被应用到模型异常检测。随后，考虑到异常数据的不确定性，形成模糊规则，并使用模糊规则来实现最终的异常数据检测。在这项研究中，合成和公开可用的数据集被认为是在实验分析。数值结果表明，该方法在ADD性能上比传统模型有8.60%~ 32.58%的优势，验证了基于粒度数据描述符的方法的可行性和有效性.此外，通过计算复杂度的讨论，该方法也被证明是有效的检测异常。
It is meaningful for data-driven studies to detect and remove the anomaly data from a given raw dataset. Considering the existing anomaly data detection (ADD) methods have some drawbacks, e.g. confidence and uncertainty in anomaly description, computation cost of kernel learning and deep learning in ADD, therefore this paper proposes an advanced approach for constructing low-cost and effective ADD detectors. Firstly, making use of the good data description ability of information granules, granular data descriptors are constructed for anomaly and normal data description. Then, based on these data descriptors, reconstruction-based strategy is applied to model anomaly detection. Subsequently, with consideration of anomaly data’s uncertainty, fuzzy rules are formed and used to realize the final anomaly data detection. In this study, both synthetic and publicly available datasets are considered in experimental analysis. Numerical results illustrate the proposed method has a superiority ranging from 8.60% to 32.58% to conventional models on ADD performance, verifying the feasibility and effectiveness the proposed methods via granular data descriptors. Moreover, through the discussion on computation complexity, the proposed method is also demonstrated efficient on detecting anomalies.