Abstract
In this book, we have presented fuzzy rough set based classification methods for various challenging types of data. We have studied class imbalanced data, semi-supervised data, multi-instance data and multi-label data. Fuzzy rough set theory allows to model the uncertainty present in data both in terms of vagueness (fuzziness) and indiscernibility or imprecision (roughness).
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Vluymans, S. (2019). Conclusions and Future Work. In: Dealing with Imbalanced and Weakly Labelled Data in Machine Learning using Fuzzy and Rough Set Methods. Studies in Computational Intelligence, vol 807. Springer, Cham. https://doi.org/10.1007/978-3-030-04663-7_8
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DOI: https://doi.org/10.1007/978-3-030-04663-7_8
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Publisher Name: Springer, Cham
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Online ISBN: 978-3-030-04663-7
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