Unsupervised hybrid anomaly detection model for logistics fleet management systems
Thananut Phiboonbanakit, Van‐Nam Huynh, Teerayut Horanont, Thepchai Supnithi · IET Intelligent Transport Systems · 2019
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.