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Knowledge Discovery Using Rough Set Theory

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Advances in Machine Learning I

Part of the book series: Studies in Computational Intelligence ((SCI,volume 262))

Abstract

Rough Set Theory (RST) opened a new direction in the development of incomplete information theories and is a powerful data analysis tool. In this investigation, the possibility of using this theory to generate a priori knowledge about a dataset is demonstrated. A proposal is developed for previous characterization of training sets, using RST estimation measurements. This characterization offers an assessment of the quality of data in order to use them as a training set in machine learning techniques. The proposal has been experimentally studied using international databases and some known classifiers such as MLP, C4.5 and K-NN, and satisfactory results have been obtained.

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Authors and Affiliations

  1. Department of Computer Science, University of Camagüey, Camagüey, Cuba

    Yaile Caballero, Leticia Arco & Enislay Ramentol

  2. Department of Computer Science, Universidad Central de Las Villas, Santa Clara, Cuba

    Rafael Bello & Maria García

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  1. Yaile Caballero
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  2. Rafael Bello
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  3. Leticia Arco
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  4. Maria García
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  5. Enislay Ramentol
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Editors and Affiliations

  1. Institute of Computer Science, Polish Academy of Sciences, ul.Ordona 21, 01-237, Warsaw, Poland

    Jacek Koronacki  & Sławomir T. Wierzchoń  & 

  2. Woodward Hall 430C University of North Carolina, 9201 University City Blvd., N.C. 28223, Charlotte, USA

    Zbigniew W. Raś

  3. Systems Research Institute, Polish Academy of Sciences, ul.Newelska 6, 01-447, Warsaw, 01-447

    Janusz Kacprzyk

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Caballero, Y., Bello, R., Arco, L., García, M., Ramentol, E. (2010). Knowledge Discovery Using Rough Set Theory. In: Koronacki, J., Raś, Z.W., Wierzchoń, S.T., Kacprzyk, J. (eds) Advances in Machine Learning I. Studies in Computational Intelligence, vol 262. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-05177-7_18

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  • DOI: https://doi.org/10.1007/978-3-642-05177-7_18

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