Process (computing)

Engineering

Ensemble learning

Fleet management

Ensemble forecasting

Systems engineering

Operating system

Anomaly detection

Telecommunications

Set (abstract data type)

Data mining

Computer science

Anomaly (physics)

Physics

Condensed matter physics

Artificial intelligence

Programming language

Unsupervised learning

Machine learning

IET Intelligent Transport Systems

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.

Unsupervised hybrid anomaly detection model for logistics fleet management systems