贝叶斯神经网络在异常值检测中的应用。

IF 2.6 4区 计算机科学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Big Data Pub Date : 2023-10-01 Epub Date: 2023-01-27 DOI:10.1089/big.2021.0343
Chen Tao
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引用次数: 1

摘要

异常检测在欺诈检测、疾病诊断和设备缺陷检测等多个领域都至关重要。随着深度学习的发展,贝叶斯神经网络异常检测成为近年来的一个新的研究课题。本文旨在提出一种广泛适用的基于不确定性测量的使用BNN的异常值检测方法(异常检测的一类)。在BNN的预测中产生了三种不确定性:认知不确定性、任意不确定性和(模型)错误指定不确定性。尽管先前研究中的方法被用来测量认识和假设的不确定性,但本文提出了一种利用损失函数来量化错误指定不确定性的新方法。然后,通过特定的组合模型将这三个不确定性源合并在一起,构建总的预测不确定性。在这项研究中,关键思想是具有高总预测不确定性的观测值应与数据中的异常值相对应。本研究方法分别应用于修改后的国家标准与技术研究所(MNIST)数据集和出租车数据集的实验。从结果来看,如果网络构造得当,训练有素,模型参数经过仔细调整,MNIST数据集中的大多数异常图像和Taxi数据集中的所有异常交通时段都可以很好地检测到。此外,将该方法的性能与之前提出的BNN异常检测方法以及经典的局部异常因子和基于密度的噪声应用空间聚类方法进行了比较。本研究将不确定性的分类本质上与异常检测联系起来,并率先考虑将不同的不确定性来源结合起来,以改变检测结果,而不是每次只使用单个不确定性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Applications of Bayesian Neural Networks in Outlier Detection.

Anomaly detection is crucial in a variety of domains, such as fraud detection, disease diagnosis, and equipment defect detection. With the development of deep learning, anomaly detection with Bayesian neural networks (BNNs) becomes a novel research topic in recent years. This article aims to propose a widely applicable method of outlier detection (a category of anomaly detection) using BNNs based on uncertainty measurement. There are three kinds of uncertainties generated in the prediction of BNNs: epistemic uncertainty, aleatoric uncertainty, and (model) misspecification uncertainty. Although the approaches in previous studies are adopted to measure epistemic and aleatoric uncertainty, a new method of utilizing loss functions to quantify misspecification uncertainty is proposed in this article. Then, these three uncertainty sources are merged together by specific combination models to construct total prediction uncertainty. In this study, the key idea is that the observations with high total prediction uncertainty should correspond to outliers in the data. The method of this research is applied to the experiments on Modified National Institute of Standards and Technology (MNIST) dataset and Taxi dataset, respectively. From the results, if the network is appropriately constructed and well-trained and model parameters are carefully tuned, most anomalous images in MNIST dataset and all the abnormal traffic periods in Taxi dataset can be nicely detected. In addition, the performance of this method is compared with the BNN anomaly detection methods proposed before and the classical Local Outlier Factor and Density-Based Spatial Clustering of Applications with Noise methods. This study links the classification of uncertainties in essence with anomaly detection and takes the lead to consider combining different uncertainty sources to reform detection outcomes instead of using only single uncertainty each time.

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来源期刊
Big Data
Big Data COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-COMPUTER SCIENCE, THEORY & METHODS
CiteScore
9.10
自引率
2.20%
发文量
60
期刊介绍: Big Data is the leading peer-reviewed journal covering the challenges and opportunities in collecting, analyzing, and disseminating vast amounts of data. The Journal addresses questions surrounding this powerful and growing field of data science and facilitates the efforts of researchers, business managers, analysts, developers, data scientists, physicists, statisticians, infrastructure developers, academics, and policymakers to improve operations, profitability, and communications within their businesses and institutions. Spanning a broad array of disciplines focusing on novel big data technologies, policies, and innovations, the Journal brings together the community to address current challenges and enforce effective efforts to organize, store, disseminate, protect, manipulate, and, most importantly, find the most effective strategies to make this incredible amount of information work to benefit society, industry, academia, and government. Big Data coverage includes: Big data industry standards, New technologies being developed specifically for big data, Data acquisition, cleaning, distribution, and best practices, Data protection, privacy, and policy, Business interests from research to product, The changing role of business intelligence, Visualization and design principles of big data infrastructures, Physical interfaces and robotics, Social networking advantages for Facebook, Twitter, Amazon, Google, etc, Opportunities around big data and how companies can harness it to their advantage.
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