Adaboost-based SVDD for anomaly detection with dictionary learning

• Published in: Expert systems with applications 2024-03, Vol.238, p.121770, Article 121770
• Main Authors: Liu, Bo, Li, Xiaokai, Xiao, Yanshan, Sun, Peng, Zhao, Shilei, Peng, Tiantian, Zheng, Zhiyu, Huang, Yongsheng
• Format: Article
• Language: English
• Subjects: Anomaly detection; Dictionary learning; Support vector data description
• ISSN: 0957-4174; 1873-6793
• DOI: 10.1016/j.eswa.2023.121770

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 combining 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. •An efficient model based on dictionary learning and SVDD for anomaly detection is proposed.•We introduce Adaboost algorithm to construct a strong classifier and present an iterative optimization process.•Some normal samples that were incorrectly identified as anomalies may contain the potential information.