Overview of ship classifications based on AIS data using machine learning methods

The table shows an overview and comparison of research on different classification methods in water transport based on Automatic Identification System (AIS) data using machine learning. Classification is the process of categorizing or grouping objects or data into different classes or categories based on their characteristics or features. Ship classification is the process of categorizing ships into different types based on their characteristics, such as size, shape, purpose, and other features. This information is important for various purposes, including safety, navigation, and regulation. Ship classification is achieved by different methods such as Support Vector Machine (SVM), k-Nearest Neighbor (kNN), Naive Bayes Classification (NBC), Decision Tree, Random Forest, Sparse Representation Classification (SRC), Neural Networks, J48, Logistic, Random Tree, Multilayer Perceptron etc. The goal of ship classification is to accurately identify and distinguish between different vessel motion patterns, which can aid in various applications such as vessel traffic management, collision avoidance, and route planning.