Identification of vessel anomaly behavior using support vector machines and Bayesian networks - Dwi Handayani, Dini Oktarina; Sediono, Wahju; Shah, Asadullah (2014)
In this work, a model based on Support Vector Machines (SVMs) classification to identify vessel anomaly behavior have been proposed and implemented, and the result is compared to Bayesian Networks (BNs). The works have been done using the real world Automated Identification System (AIS) vesselreporting data. SVMs can achieve higher accuracy compared to BNs in both memory-test and blind-test. The effect of holdout method which is partitioned size of training and testing data set on the accuracy result were also investigated in this study. The proposed classifier demonstrated to be a viable tool for identifying the vessel anomaly behavior by its high accuracy.