Title: Unsupervised feature selection using rough set and teaching learning-based optimisation
Authors: Suresh Chandra Satapathy; Anima Naik; K. Parvathi
Addresses: Department of CSE, Anil Neerukonda Institute of Technology and Sciences, Sangivalasa, Vishakapatnam, 531162, India ' Department of Computer Science, MITS, Rayagada, Odisha, 765017, India ' Department of Electronics and Communication Engineering, CUTM, Paralakhemundi, Odisha, 761211, India
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.
Keywords: feature selection; dimensionality reduction; rough sets; teaching learning-based optimisation; TLBO; clustering alrogithms; data analysis; information systems; data reduction.
International Journal of Artificial Intelligence and Soft Computing, 2013 Vol.3 No.3, pp.244 - 256
Received: 06 Jun 2012
Accepted: 24 Nov 2012
Published online: 12 Jul 2014 *