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An Innovative Approach for Attribute Reduction Using Rough Sets and Flower Pollination Optimisation

Yamany, Waleed, Emary, Eid, Hassanien, Aboul Ella, Schaefer, Gerald and Zhu, Shao Ying (2016) An Innovative Approach for Attribute Reduction Using Rough Sets and Flower Pollination Optimisation. Procedia Computer Science, 96. pp. 403-409.

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Optimal search is a major challenge for wrapper-based attribute reduction. Rough sets have been used with much success, but current hill-climbing rough set approaches to attribute reduction are insufficient for finding optimal solutions. In this paper, we propose an innovative use of an intelligent optimisation method, namely the flower search algorithm (FSA), with rough sets for attribute reduction. FSA is a relatively recent computational intelligence algorithm, which is inspired by the pollination process of flowers. For many applications, the attribute space, besides being very large, is also rough with many different local minima which makes it difficult to converge towards an optimal solution. FSA can adaptively search the attribute space for optimal attribute combinations that maximise a given fitness function, with the fitness function used in our work being rough set-based classification. Experimental results on various benchmark datasets from the UCI repository confirm our technique to perform well in comparison with competing methods.

Item Type: Article
Status: Published
DOI: https://doi.org/10.1016/j.procs.2016.08.083
School/Department: School of Science, Technology and Health
URI: https://ray.yorksj.ac.uk/id/eprint/9928

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