Abstract
Feature selection refers to the selection of input attributes that are most predictive of a given outcome. This is a problem encountered in many areas such as machine learning, signal processing, and recently bioinformatics/computational biology. Feature selection is one of the most important and challenging tasks, when it comes to dealing with large datasets with tens or hundreds of thousands of variables. Areas of web-mining and gene expression array analysis provide examples, where selection of interesting and useful features determines the performance of subsequent analysis. The intrinsic nature of noise, uncertainty, incompleteness of data makes extraction of hidden and useful information very difficult. Capability of handling imprecision, inexactness and noise, has attracted researchers to use rough sets for feature selection. This article provides an overview on recent literature in this direction.
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References
A. Anand. Representation and learning of inexact information using rough set theory. Master's thesis, Department of Mathematics, Indian Institute of Technology, Kanpur, India, 2002.
M. Banerjee, S. Mitra, and H. Banka. Evolutionary-rough feature selection in gene expression data. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, 2005. Accepted.
A. T. Bjorvand. ‘Rough Enough’ – A system supporting the rough sets approach. In Proceedings of the Sixth Scandinavian Conference on Artificial Intelligence, pages 290–291, Helsinki, Finland, 1997.
L. Cao, H. P. Lee, C. K. Seng, and Q. Gu. Saliency analysis of support vector machines for gene selection in tissue classification. Neural Computing and Applications, 11:244–249, 2003.
V. Chankong and Y. Y. Haimes. Multiobjective Decision Making Theory and Methodology. North-Holland, 1983.
F. Chu, W. Xie, and L. Wang. Gene selection and cancer classification using a fuzzy neural network. In Proceedings of 2004 Annual Meeting of the North American Fuzzy Information Processing Society (NAFIPS 2004), volume 2, pages 555–559, 2004.
K. Deb. Multi-Objective Optimization using Evolutionary Algorithms. John Wiley & Sons, London, 2001.
K. Deb, S. Agarwal, A. Pratap, and T. Meyarivan. A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6:182–197, 2002.
D.E. Goldberg. Genetic Algorithms for Search, Optimization, and Machine Learning. Addison-Wesley, Reading, 1989.
C. -J. Huang. Class prediction of cancer using probabilistic neural networks and relative correlation metric. Applied Artificial Intelligence, 18:117–128, 2004.
R. Jensen. Combining rough and fuzzy sets for feature selection. PhD thesis, School of Informatics, University of Edinburgh, 2004.
J. Komorowski, A. Øhrn, and A. Skowron. The rosetta rough set software system. In W. Klasgen and J. Zytkow, editors, Handbook of Data Mining and Knowledge Discovery, chapter D.2.3. Oxford University Press, 2002.
Z. Pawlak. Rough sets. International J. Comp & Inf. Sc., 1982.
Z. Pawlak. Rough Sets, Theoretical Aspects of Reasoning about Data. Kluwer Academic Publishers, Dordrecht, 1991.
A. Skowron and C. Rauszer. The discernibility matrices and functions in information systems. In R. Slowinski, editor, Handbook of Applications and Advances of the Rough Set Theory, pages 331–362. Kluwer Academic Publishers, Dordrecht, 1992.
S. Vinterbo and A. Øhrn. Minimal approximate hitting sets and rule templates. International Journal of Approximate Reasoning, pages 123–143, 2000.
J. Wroblewski. Finding minimal reducts using genetic algorithms. In Second Annual Joint Conference on Information Sciences, pages 186–189, 1995.
L. Yu and H. Liu. Efficient feature selection via analysis of relevance and redundancy. Journal of Machine Learning Research, 5:1205–1224, 2004.
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Banerjee, M., Mitra, S., Anand, A. (2006). Feature Selection Using Rough Sets. In: Jin, Y. (eds) Multi-Objective Machine Learning. Studies in Computational Intelligence, vol 16. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-33019-4_1
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DOI: https://doi.org/10.1007/3-540-33019-4_1
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