Abstract
Reducing the redundant attributes is an important preprocessing step in data mining. In the paper, a novel search algorithm COREplusAFSA for minimal attribute set reduction based on rough set theory and artificial fish swarm algorithm is proposed. First, the algorithm identifies the attributes from the core. Second, the artificial fish swarm algorithm is applied. Some well-known data sets from UC Irvine Machine Learning Repository were selected to verify the proposed algorithm. The results of experiments show that the investigated method COREplusAFSA is a better solution to the attribute set reduction problem than the application of only artificial fish swarm algorithm.
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Acknowledgments
The work was supported by the grant WZ/WI-IIT/2/2020 from Bialystok University of Technology and funded with resources for research by the Ministry of Science and Higher Education in Poland. I would like to thank Mr. Mateusz Walendziuk for the implementation of the algorithm.
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Stepaniuk, J. (2021). Core Computation and Artificial Fish Swarm Algorithm in Rough Set Data Reduction. In: Saeed, K., Dvorský, J. (eds) Computer Information Systems and Industrial Management. CISIM 2021. Lecture Notes in Computer Science(), vol 12883. Springer, Cham. https://doi.org/10.1007/978-3-030-84340-3_37
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DOI: https://doi.org/10.1007/978-3-030-84340-3_37
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