Deep Learning Versus Traditional Solutions for Group Trajectory Outliers.
IEEE Trans Cybern
; 52(6): 4508-4519, 2022 Jun.
Article
en En
| MEDLINE
| ID: mdl-33201830
ABSTRACT
This article introduces a new model to identify a group of trajectory outliers from a large trajectory database and proposes several algorithms. These can be split into three categories 1) algorithms based on data mining and knowledge discovery, which study the different correlations among the trajectory data and identify the group of abnormal trajectories from the knowledge extracted; 2) algorithms based on machine learning and computational intelligence methods, which use the ensemble learning and metaheuristics to find the group of trajectory outliers; and 3) an algorithm exploring the convolution deep neural network that learns the different features of historical data to determine the group of trajectory outliers. Experiments on different trajectory databases have been carried out to investigate the proposed algorithms. The results show that the deep learning solution outperforms data mining, machine learning, and computational intelligence solutions, as well as state-of-the-art solutions in terms of runtime and accuracy performance.
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Aprendizaje Profundo
Tipo de estudio:
Prognostic_studies
Idioma:
En
Revista:
IEEE Trans Cybern
Año:
2022
Tipo del documento:
Article