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Unsupervised hybrid anomaly detection model for logistics fleet management systems
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Document Title
Unsupervised hybrid anomaly detection model for logistics fleet management systems
Author
Phiboonbanakit T., Huynh V.-N., Horanont T., Supnithi T.
Name from Authors Collection
Affiliations
Japan Advanced Institute of Science and Technology, Ishikawa, Japan; Sirindhorn International Institute of Technology, Pathum Thani, Thailand; NECTEC, National Science and Technology Development Agency, Pathum Thani, Thailand
Type
Article
Source Title
IET Intelligent Transport Systems
ISSN
1751956X
Year
2019
Volume
13
Issue
11
Page
1636-1648
Open Access
Bronze
Publisher
Institution of Engineering and Technology
DOI
10.1049/iet-its.2019.0167
Abstract
Unsupervised anomaly detection in high-dimensional data is crucial for both machine learning research and industrial applications. Over the past few years, the logistics agencies' operation efficiency decreased due to the lack of understanding how best to handle potential client requests, while current anomaly detection approaches might be inefficient in distinguishing normal and abnormal behaviours from the high-dimensional data. Although previous studies continue to improve detection models, they suffer from the inability to preserve vital information while performing a dimensional reduction process. In this study, the authors aim to improve anomaly detection by proposing an ensemble method that is built and trained on two hybrid models. Eventually, after two trained hybrid models were introduced, an ensemble probability rule was applied to combine their prediction results for performing final decision-making of anomaly detection. To demonstrate the practical use of our proposed model, we have set up a case study with a logistics agency and the experiment shows that the proposed model improved accuracy by 0.88 over current models. © The Institution of Engineering and Technology 2019
Industrial Classification
Knowledge Taxonomy Level 1
Knowledge Taxonomy Level 2
Knowledge Taxonomy Level 3
Funding Sponsor
Office of Naval Research Global; Thammasat University; Sirindhorn International Institute of Technology, Thammasat University
License
CC BY
Rights
Author
Publication Source
Scopus