Diversity Aware-Based Sequential Ensemble Learning for Robust Anomaly Detection
Anomaly detection has become a popular topic in many domains because anomalies can provide valuable information. Recently, ensemble learning has been applied to improve the generalization ability of existing anomaly detection methods. In an anomaly ensemble framework, diversity is essential for buil...
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doaj-ec03230d41064681b9eb54f4f11d25ca2021-03-30T02:09:06ZengIEEEIEEE Access2169-35362020-01-018423494236310.1109/ACCESS.2020.29768509016158Diversity Aware-Based Sequential Ensemble Learning for Robust Anomaly DetectionJia Zhang0https://orcid.org/0000-0001-5740-715XZhiyong Li1https://orcid.org/0000-0001-9720-5915Shaomiao Chen2https://orcid.org/0000-0002-8123-5463College of Computer Science and Electronic Engineering, Hunan University, Changsha, ChinaCollege of Computer Science and Electronic Engineering, Hunan University, Changsha, ChinaCollege of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan, ChinaAnomaly detection has become a popular topic in many domains because anomalies can provide valuable information. Recently, ensemble learning has been applied to improve the generalization ability of existing anomaly detection methods. In an anomaly ensemble framework, diversity is essential for building a powerful ensemble method to obtain impressive performance. However, most studies use heuristic techniques to improve the diversity of ensembles, and it generally leads to limited diversity. To obtain improved diversity, we propose a diversity aware-based sequential ensemble method (called D-SEM) for anomaly detection. Specifically, our proposed method divides the ensemble diversity into two parts: sample diversity and model diversity. For sample diversity, we introduce the subsampling technique to implement preliminary generation of diverse datasets for training. For model diversity, we design an ensemble-based optimization model to learn base classifiers with improved diversity. Furthermore, an unsupervised diversity measure is proposed to quantitatively assess diversity and an anomaly pruning strategy is utilized to successively eliminate pseudo-anomalies. Based on the inclusion of sample diversity and model diversity, the proposed D-SEM method obtains better generalization ability for anomaly detection. The experimental results based on real-world datasets suggest that the proposed method has superior performance compared with various state-of-the-art methods.https://ieeexplore.ieee.org/document/9016158/Anomaly detectionensemble learningdiversitygeneralization ability |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Jia Zhang Zhiyong Li Shaomiao Chen |
spellingShingle |
Jia Zhang Zhiyong Li Shaomiao Chen Diversity Aware-Based Sequential Ensemble Learning for Robust Anomaly Detection IEEE Access Anomaly detection ensemble learning diversity generalization ability |
author_facet |
Jia Zhang Zhiyong Li Shaomiao Chen |
author_sort |
Jia Zhang |
title |
Diversity Aware-Based Sequential Ensemble Learning for Robust Anomaly Detection |
title_short |
Diversity Aware-Based Sequential Ensemble Learning for Robust Anomaly Detection |
title_full |
Diversity Aware-Based Sequential Ensemble Learning for Robust Anomaly Detection |
title_fullStr |
Diversity Aware-Based Sequential Ensemble Learning for Robust Anomaly Detection |
title_full_unstemmed |
Diversity Aware-Based Sequential Ensemble Learning for Robust Anomaly Detection |
title_sort |
diversity aware-based sequential ensemble learning for robust anomaly detection |
publisher |
IEEE |
series |
IEEE Access |
issn |
2169-3536 |
publishDate |
2020-01-01 |
description |
Anomaly detection has become a popular topic in many domains because anomalies can provide valuable information. Recently, ensemble learning has been applied to improve the generalization ability of existing anomaly detection methods. In an anomaly ensemble framework, diversity is essential for building a powerful ensemble method to obtain impressive performance. However, most studies use heuristic techniques to improve the diversity of ensembles, and it generally leads to limited diversity. To obtain improved diversity, we propose a diversity aware-based sequential ensemble method (called D-SEM) for anomaly detection. Specifically, our proposed method divides the ensemble diversity into two parts: sample diversity and model diversity. For sample diversity, we introduce the subsampling technique to implement preliminary generation of diverse datasets for training. For model diversity, we design an ensemble-based optimization model to learn base classifiers with improved diversity. Furthermore, an unsupervised diversity measure is proposed to quantitatively assess diversity and an anomaly pruning strategy is utilized to successively eliminate pseudo-anomalies. Based on the inclusion of sample diversity and model diversity, the proposed D-SEM method obtains better generalization ability for anomaly detection. The experimental results based on real-world datasets suggest that the proposed method has superior performance compared with various state-of-the-art methods. |
topic |
Anomaly detection ensemble learning diversity generalization ability |
url |
https://ieeexplore.ieee.org/document/9016158/ |
work_keys_str_mv |
AT jiazhang diversityawarebasedsequentialensemblelearningforrobustanomalydetection AT zhiyongli diversityawarebasedsequentialensemblelearningforrobustanomalydetection AT shaomiaochen diversityawarebasedsequentialensemblelearningforrobustanomalydetection |
_version_ |
1724185795657465856 |