Unsupervised feature selection using rough set and teaching learning-based optimisation Online publication date: Sat, 12-Jul-2014
by Suresh Chandra Satapathy; Anima Naik; K. Parvathi
International Journal of Artificial Intelligence and Soft Computing (IJAISC), Vol. 3, No. 3, 2013
Abstract: Feature selection is a valuable technique in data analysis for information preserving data reduction. This paper proposes to consider an information system without any decision attribute. The proposal is useful when we get unlabeled data, which contains only input information (condition attributes) but without decision (class attribute). TLBO clustering algorithm is applied to cluster the given information. Decision table could be formulated using this clustered data as the decision variable. Then rough set and TLBO algorithms are applied for selecting features. The experiments are carried out on datasets of UCI machine repository and from the website http://www.ailab.si/orange/datasets.asp to analyse the performance study of our proposed approach with other approaches like genetic algorithm, particle swarm optimisation and differential evolution techniques. The results clearly reveal that our proposed approach outperforms other approaches investigated in this paper.
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 Artificial Intelligence and Soft Computing (IJAISC):
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