Abstract
Attribute reduction is used to allow elimination of redundant attributes while remaining full meaning of the original dataset. Rough sets have been used as attribute reduction techniques with much success. However, rough set applies to attribute reduction are inadequate at finding optimal reductions. This paper proposes an optimal attribute reduction strategy relying on rough sets and discrete firefly algorithm. To demonstrate the applicability and superiority of the proposed model, comparison between the proposed models with existing well-known methods is also investigated. The experiment results illustrate that performances of the proposed model when compared to other attribute reduction can provide comparative solutions efficiently.
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Long, N.C., Meesad, P., Unger, H. (2014). Attribute Reduction Based on Rough Sets and the Discrete Firefly Algorithm. In: Boonkrong, S., Unger, H., Meesad, P. (eds) Recent Advances in Information and Communication Technology. Advances in Intelligent Systems and Computing, vol 265. Springer, Cham. https://doi.org/10.1007/978-3-319-06538-0_2
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DOI: https://doi.org/10.1007/978-3-319-06538-0_2
Publisher Name: Springer, Cham
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