Abstract
Classification and regression tree (CART) has been widely used in data mining to solve classification and prediction problems. In this paper, we propose a novel numerical ant-colony decision tree (nACDT) algorithm that combines CART with ant-colony optimisation (ACO). The combination works not only in inducing decision trees but also in incorporating the discretisation of attributes during the process to cope with continuous attributes. The proposed algorithm is used to estimate the duration of ship maintenance, with the aim of improving service quality and competitive advantage in the shipyard industry. The results show that the predictive accuracy of the proposed algorithm is statistically significantly higher than the accuracy of CART, which is the most well-known conventional decision tree algorithm.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: International Journal of Applied Management Science
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
