Binary whale optimisation: an effective swarm algorithm for feature selection Online publication date: Sun, 20-May-2018
by Heba F. Eid
International Journal of Metaheuristics (IJMHEUR), Vol. 7, No. 1, 2018
Abstract: Feature selection process is considered as one of the most difficult challenges in machine learning and has attracted many researchers recently. The main disadvantages of the classical optimisation algorithms based feature selection are slow convergence speed and local optima stagnation. In this work, a novel binary version of the whale optimisation is proposed for selecting the optimal feature subset and increasing the classification accuracy. The performance of the proposed binary whale optimisation (BWO) is verified by comparisons with three well known optimisation based feature selection algorithms; genetic algorithm, ant colony optimisation and particle swarm optimisation; on nine benchmark datasets. The qualitative and quantitative results show the capability of the proposed BWO to search the feature space for optimal feature combinations. Moreover, results prove that the proposed BWO is able to outperform the current algorithms on the majority of datasets in terms of both average classification accuracy and convergence speed.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
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 Metaheuristics (IJMHEUR):
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 subs@inderscience.com
Our Blog 
Follow us on Twitter 
Visit us on Facebook