Abstract
The imbalanced data problem turned out to be one of the most important and challenging problems in artificial intelligence. We discuss an approach of minority class approximation based on rough set methods and three-way decision. This approach seems to be more general than the traditional one. However, it requires developing some new logical tools for reasoning based on rough sets and three-way decision, which is often expressed in natural language.
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The work was supported by the grant from Bialystok University of Technology and funded with resources for research by the Ministry of Science and Higher Education in Poland.
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Stepaniuk, J. (2020). Imbalanced Data: Rough Set Methods in Approximation of Minority Classes. In: Saeed, K., Dvorský, J. (eds) Computer Information Systems and Industrial Management. CISIM 2020. Lecture Notes in Computer Science(), vol 12133. Springer, Cham. https://doi.org/10.1007/978-3-030-47679-3_38
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DOI: https://doi.org/10.1007/978-3-030-47679-3_38
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