Group GIAA, University Carlos III of Madrid, 28270 Madrid, Spain.
Sensors (Basel). 2020 Jul 6;20(13):3782. doi: 10.3390/s20133782.
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.
本文提出了一种管理真实运动学数据和检测渔船的数据准备过程。该解决方案是一种二进制分类，将船舶轨迹分类为捕捞或非捕捞船舶。所使用的数据具有在使用真实世界数据的经典数据挖掘应用中发现的典型问题，例如噪声和不一致性。这两个类在数据中也明显不平衡，这个问题可以通过重新采样实例的算法来解决。对于分类，从船舶轨迹的时空数据中提取了一系列特征，这些特征可从自动识别系统（AIS）报告的序列中获得。这些特征是为船舶行为建模而提出的，但由于它们不包含与上下文相关的信息，因此可以将分类应用于其他场景。实验表明，所提出的数据准备过程对所提出的分类问题是有用的。此外，仅使用最少的信息就可以获得积极的结果。
