Sun, H. and Hu, X. (2017) Attribute Selection for Decision Tree Learning with Class Constraints. Chemometrics and Intelligent Laboratory Systems, 163, 16-23.
https://doi.org/10.1016/j.chemolab.2017.02.004

has been cited by the following article:

• TITLE: Increasing Efficiency of an Intelligent Semaphore by Implementing an ID3 Approach in Decision-Making

  AUTHORS: J. A. Castán Rocha, S. Ibarra Martínez, J. Laria Menchaca, J. D. Terán Villanueva, M. G. Treviño Berrones, J. Pérez Cobos, E. Castán Rocha

  KEYWORDS: Urban Traffic Control, Intelligent Transport System, ID3 Approach, Vehicular Mobility

  JOURNAL NAME: Journal of Computer and Communications, Vol.6 No.1, January 30, 2018

  ABSTRACT: The main objective of a semaphore is to provide a correct and fluent vehicular mobility. Many countries around the world are using such devices in urban areas. However, the traditional semaphore operative ways are outdated. We report in a previous contribution the development of a methodology capable of improving the vehicular mobility by proposing a new green light interval based on road conditions with a CBR approach. However, this proposal should include whether it is needed to modify such light duration. To do this, the paper proposes the adaptation of an inductive decision tree helping to decide when the semaphore should try to fix the green light interval according to specific road requirements. Some experiments are conducted in a simulated environment to evaluate the pertinence of implementing a decision-making before the CBR methodology. For example, using the ID3 approach the decisions of the system improve almost 8% in a set of 10,000 experiments. Finally, some conclusions are drawn to emphasize the benefits of including this technique in a methodology to implement intelligent semaphores.