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2020 | OriginalPaper | Chapter

Imbalanced Data: Rough Set Methods in Approximation of Minority Classes

Author : Jaroslaw Stepaniuk

Published in: Computer Information Systems and Industrial Management

Publisher: Springer International Publishing

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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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10.

Stepaniuk, J., Góra, G., Skowron, A.: Concept approximation based on rough sets and judgment. In: Mihálydeák, T., Min, F., Wang, G., Banerjee, M., Düntsch, I., Suraj, Z., Ciucci, D. (eds.) IJCRS 2019. LNCS (LNAI), vol. 11499, pp. 16–27. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-22815-6_2CrossRef

Title: Imbalanced Data: Rough Set Methods in Approximation of Minority Classes
Author: Jaroslaw Stepaniuk
Publisher: Springer International Publishing
Book: Computer Information Systems and Industrial Management
Print ISBN: 978-3-030-47678-6

Electronic ISBN: 978-3-030-47679-3

Copyright Year: 2020
DOI: https://doi.org/10.1007/978-3-030-47679-3_38

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