Solving feature selection problem using intelligent double treatment iterative composite neighbourhood structure algorithm Online publication date: Fri, 31-Mar-2017
by Saif Kifah; Salwani Abdullah; Yahya Z. Arajy
International Journal of Computational Vision and Robotics (IJCVR), Vol. 7, No. 3, 2017
Abstract: Attribute reduction is one of the main contributions in rough set theory (RST) that tries to discover all possible reducts by eliminating redundant attributes while maintaining the information of the problem in hand. In this paper, we propose a meta-heuristic methodology called a double treatment iterative improvement algorithm with intelligent selection of composite neighbourhood structure, to solve the attribute reduction problems and to obtain near optimal reducts. The algorithm works iteratively by only accepting an improved solution. The proposed approach has been tried on a set of 13 benchmark datasets taken from the University of California Irvine (UCI) machine learning repository in line with the state-of-the-art methods. Thirteen datasets have been chosen due to the differences in size and complexity in order to test the stability of the proposed algorithm. The experimental results demonstrate that the proposed approach is able to produce competitive results for the tested datasets.
Online publication date: Fri, 31-Mar-2017
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Computational Vision and Robotics (IJCVR):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email firstname.lastname@example.org