Dealing with Imbalanced Data for GPS Trajectory Outlier Detection

• Autorzy: Van-Chau Nguyen , Anh Le Van Quoc , Van Chien Nguyen

Identyfikatory

DOI

10.15439/2022R10

• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

component; formatting; style; styling; insert

• Wydawca: Polskie Towarzystwo Informatyczne
• Czasopismo: Annals of Computer Science and Information Systems
• Rocznik: 2022
• Tom: Vol. 33
• Strony: 69--74
• Opis fizyczny: Bibliogr. 11 poz., rys., tab., wykr.

Twórcy

autor

Van-Chau Nguyen

• Faculty of Information Technology Hung Yen University of Technology and Education Hung Yen, Vietnam

• https://orcid.org/haunv%40utehy.edu.vn

autor

Anh Le Van Quoc

• Faculty of Information Technology Ho Chi Minh City University of Transport Ho Chi Minh City, Vietnam

autor

Van Chien Nguyen

• Faculty of Information Technology Ho Chi Minh City University of Transport Ho Chi Minh City, Vietnam

Bibliografia

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