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6 results on '"Sánchez Pedroche, David"'

1. Compression of Clustered Ship Trajectories for Context Learning and Anomaly Detection

Author

Sánchez Pedroche, David, García, Jesús, Molina, José Manuel, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, García Bringas, Pablo, editor, Pérez García, Hilde, editor, Martinez-de-Pison, Francisco Javier, editor, Villar Flecha, José Ramón, editor, Troncoso Lora, Alicia, editor, de la Cal, Enrique A., editor, Herrero, Álvaro, editor, Martínez Álvarez, Francisco, editor, Psaila, Giuseppe, editor, Quintián, Héctor, editor, and Corchado Rodriguez, Emilio S., editor

Published
2023
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5. Architecture for trajectory-based fishing ships 3 classification with AIS data

Author

Sánchez Pedroche, David, Amigo Herrero, Daniel, García Herrero, Jesús, Molina López, José Manuel, and Ministerio de Economía y Competitividad (España)

Subjects
AIS data, Informática, Class imbalance, Machine learning, Spatiotemporal data mining, Data fusion, Real-world data, Trajectory classification
Abstract

This paper proposes a data preparation process for managing real-world kinematic data and detecting fishing vessels. The solution is a binary classification that classifies ship trajectories into either fishing or non-fishing ships. The data used are characterized by the typical problems found in classic data mining applications using real-world data, such as noise and inconsistencies. The two classes are also clearly unbalanced in the data, a problem which is addressed using algorithms that resample the instances. For classification, a series of features are extracted from spatiotemporal data that represent the trajectories of the ships, available from sequences of Automatic Identification System (AIS) reports. These features are proposed for the modelling of ship behavior but, because they do not contain context-related information, the classification can be applied in other scenarios. Experimentation shows that the proposed data preparation process is useful for the presented classification problem. In addition, positive results are obtained using minimal information. This research was funded by public research projects of Spanish Ministry of Economy and Competitivity (MINECO), reference TEC2017-88048-C2-2-R.

Published
2020

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