| dc.description.abstract | Being an island state, Taiwan’s maritime traffic and sea transportation is well-developed; however, smuggling by sea converts into a critical issue. Coast Guard Administration (CGA) becomes a vital intercept line to curb smuggling events for Taiwan’s social and national security. Yet, it is dubious and untrustworthy for current practices since inspectors of CGA check vessels selectively by personnel experience. In addition, the repercussion caused by different smuggling items (such as fishery and guns) is varied.
In order to improve the identification rate of smuggling vessels for smuggling items (such as fishery, guns, drugs etc.) and collecting variables (such as the home port, the age of boat owners, the vessels’ entry and departure etc.) between relationships of dependence or independence, this research applies classification trees and Bayesian network algorithms respectively creating four models: a dependent classification tree model, an independent classification tree model, a dependent Bayesian network model, and an independent Bayesian network model. In particular, this research offers an inspective method for vessel’s selection owing to the impact of social security caused by smuggled items. Therefore, this research on one hand improves the identification accuracy of smuggling vessels, on the other, reduces the possible damage in social and national security by selecting the vessels with the largest smuggling value. | en_US |