异常检测
计算机科学
阿达布思
模式识别(心理学)
人工智能
支持向量机
超球体
分类器(UML)
数据挖掘
机器学习
作者
Bo Liu,X. Li,Yanshan Xiao,Peng Sun,Shilei Zhao,Tiantian Peng,Zhiyu Zheng,Yongsheng Huang
标识
DOI:10.1016/j.eswa.2023.121770
摘要
Anomaly detection aims to identify unusual behavior or discriminate abnormal samples by referring to the normal samples of data. Most exiting anomaly detection approaches train the model using only the normal data due to the scarcity of anomalies. However, the negative data or anomalies do occur in many practical applications. In this paper, we propose a novel anomaly detection method called AdaDL-SVDD for addressing uncertain data problem. In this method, both normal and anomalous samples are utilized to generate sparse representations with dictionary learning in the training phase. Meanwhile, we incorporate Support Vector Data Description (SVDD) into framework to construct a minimum hypersphere for anomaly detection over the test data. Additionally, the AdaBoost method is considered to construct a strong classifier via combing the weak classifiers. In the end, the experimental results demonstrate that the proposed AdaDL-SVDD method achieves superior performance over the UCI datasets with uncertainty and noise.
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