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Thriving Rough Sets pp 87–118Cite as

Rough Sets in Machine Learning: A Review

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Part of the book series: Studies in Computational Intelligence ((SCI,volume 708))

Abstract

This chapter emphasizes on the role played by rough set theory (RST) within the broad field of Machine Learning (ML). As a sound data analysis and knowledge discovery paradigm, RST has much to offer to the ML community. We surveyed the existing literature and reported on the most relevant RST theoretical developments and applications in this area. The review starts with RST in the context of data preprocessing (discretization, feature selection, instance selection and meta-learning) as well as the generation of both descriptive and predictive knowledge via decision rule induction, association rule mining and clustering. Afterward, we examined several special ML scenarios in which RST has been recently introduced, such as imbalanced classification, multi-label classification, dynamic/incremental learning, Big Data analysis and cost-sensitive learning.

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Authors and Affiliations

  1. Department of Computer Science, Universidad Central de Las Villas, Carretera Camajuaní km 5.5, Santa Clara, Cuba

    Rafael Bello

  2. Research & Engineering Division, Larus Technologies Corporation, 170 Laurier Ave West - Suite 310, Ottawa, ON, Canada

    Rafael Falcon

  3. School of Electrical Engineering and Computer Science, University of Ottawa, 800 King Edward Ave, Ottawa, ON, Canada

    Rafael Falcon

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  1. Rafael Bello
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Editors and Affiliations

  1. Chongqing Key Laboratory of Computational Intelligence, Chongqing University of Posts and Telecommunications, Chongqing, China

    Guoyin Wang

  2. Institute of Mathematics, University of Warsaw Institute of Mathematics, Warsaw, Poland

    Andrzej Skowron

  3. University of Regina, Department of Computer Science University of Regina, Regina, Saskatchewan, Canada

    Yiyu Yao

  4. Institute of Informatics, University of Warsaw, Warsaw, Poland

    Dominik Ślęzak

  5. Polish-Japanese Academy IT , Warsaw, Poland

    Lech Polkowski

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Bello, R., Falcon, R. (2017). Rough Sets in Machine Learning: A Review. In: Wang, G., Skowron, A., Yao, Y., Ślęzak, D., Polkowski, L. (eds) Thriving Rough Sets. Studies in Computational Intelligence, vol 708. Springer, Cham. https://doi.org/10.1007/978-3-319-54966-8_5

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