双编码半监督异常检测模型  

Dually Encoded Semi-supervised Anomaly Detection

作　　者：李辉[1] 李文根 关佶红[1] LI Hui;LI Wengen;GUAN Jihong(College of Electronic and Information Engineering,Tongji University,Shanghai 201804,China)

机构地区：[1]同济大学电子与信息工程学院,上海201804

出　　处：《计算机科学》2023年第7期53-59,共7页Computer Science

基　　金：上海市浦江人才计划项目(20PJ1414300);国家自然科学基金联合基金重点支持项目(U1936205);科技部重点研发计划(2021YFC3300300)。

摘　　要：异常检测是机器学习领域广泛研究的一个热点问题,对于工业生产、食品安全、疾病监测等都具有重要作用。当前最新的异常检测方法多基于少量可用的有标记样本和大量无标记样本联合训练半监督检测模型。然而,现有的半监督异常检测模型多采用深度学习框架,在低维数据集上由于缺少足够多的特征信息,难以学习到准确的数据边界,检测性能不佳。针对该问题,提出了双编码半监督异常检测模型(Dually Encoded Semi-supervised Anomaly Detection, DE-SAD),充分利用可获得的少部分有标记数据结合大量无标记数据进行半监督学习,通过双编码阶段约束模型学习更准确的正常数据隐含流形分布,有效拉大了正常数据和异常数据的差距。DE-SAD在来自不同领域的多个异常检测数据集上都表现出优越的异常检测性能,在低维数据上的检测性能尤为突出,其AUROC指标相比当前最优的异常检测方法最高提升了4.6%。Anomaly detection is a hot topic that has been widely studied in the field of machine learning and plays an important role in industrial production,food safety,disease monitoring,etc.The latest anomaly detection methods mostly jointly train semi-supervised detection models based on a small number of available labeled samples and many unlabeled samples.However,these existing semi-supervised anomaly detection models mostly use deep learning frameworks.Due to the lack of enough feature information on low-dimensional data sets,it is difficult to learn accurate data boundaries,resulting in insufficient detection perfor-mance.To solve this problem,a dually encoded semi-supervised anomaly detection(DE-SAD)model is proposed.DE-SAD can make full use of a small amount of available labeled data and a large amount of unlabeled data for semi-supervised learning,and learn more accurate implicit manifold distribution of normal data through the dually encoded stage constraint,thus effectively magnifying the gap between normal data and abnormal data.DE-SAD shows excellent ano-maly detection performance on multiple anomaly detection datasets from different fields,especially on low-dimensional data,and its AUROC is up to 4.6%higher than the current state-of-the-art methods.

关 键 词：异常检测 半监督学习 自编码器 低维数据 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]