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Dealing with Imbalanced Data for GPS Trajectory Outlier Detection

Van-Hau Nguyen, Anh Le Van Quoc, Van Chien Nguyen

Annals of Computer Science and Information Systems

2022

Vol. 33

69--74

Detecting abnormal GPS trajectories derived by the mobility of people, cars, buses, and taxis plays a crucial role in developing applications for intelligent transportation systems. Outlier detection based on classification models is among promising approaches but it faces the imbalanced data problem, where instances labeled as abnormal have a very low number of observations. In this paper, we propose a framework that employs methods to deal with imbalanced data to the problem of GPS trajectory outlier detection. Our experiments show that dealing with imbalanced data beforehand can improve the performance of outlier detection models.