Identification of vessel anomaly behavior using support vector machines and Bayesian networks
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) ve...
| Main Authors: | , , |
|---|---|
| Format: | Conference or Workshop Item |
| Language: | English English English |
| Published: |
2014
|
| Subjects: | |
| Online Access: | http://irep.iium.edu.my/38408/ http://irep.iium.edu.my/38408/ http://irep.iium.edu.my/38408/1/p.1080.ICCCE.2014.pdf http://irep.iium.edu.my/38408/4/Sessions.pdf http://irep.iium.edu.my/38408/7/38408_Identification%20of%20vessel%20anomaly%20behavior_Scopus.pdf |
| Summary: | 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. |
|---|