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Measuring Data Imperfection in a Neighborhood Based Method

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Advances in Artificial Intelligence (CAEPIA 2015)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9422))

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Abstract

In this paper, we present an extension of k nearest neighbors method so it can perform imputation/classification from datasets with low quality data. The method performs a weighting of neighbors based on their imperfection and distance of classes. Thus the method allows us explicitly to indicate the average degree of imperfection of the neighbors that it is accepted to carry out the imputation/classification and the average distance of classes to the class of example to impute/classify that it is allowed. We carry out several experiments with both real-world and synthetic datasets with low quality data to test the proposed method.

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Acknowledgements

Supported by the projects TIN2011-27696-C02-02, TIN2014-52099-R and TIN2014-56381-REDT (“Red de Lógica Difusa y Soft Computing (LODISCO)”) of the Ministry of Economy and Competitiveness of Spain.

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Authors and Affiliations

  1. Department of Information Engineering and Communication, University of Murcia, Murcia, Spain

    José M. Cadenas & M. Carmen Garrido

  2. Department of Computer Engineering, Catholic University of San Antonio, Murcia, Spain

    Raquel Martínez

Authors
  1. José M. Cadenas
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  2. M. Carmen Garrido
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  3. Raquel Martínez
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Corresponding author

Correspondence to Raquel Martínez .

Editor information

Editors and Affiliations

  1. University of Castilla-La Mancha, Albacete, Spain

    José M. Puerta

  2. University of Castilla-La Mancha, Albacete, Spain

    José A. Gámez

  3. University of Cadiz, Cadiz, Spain

    Bernabe Dorronsoro

  4. Public University of Navarre, Pamplona, Spain

    Edurne Barrenechea

  5. Pablo de Olavide University, Sevilla, Spain

    Alicia Troncoso

  6. Department of Civil Engineering, University of Burgos, Burgos, Spain

    Bruno Baruque

  7. Public University of Navarre, Pamplona, Spain

    Mikel Galar

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Cadenas, J.M., Garrido, M.C., Martínez, R. (2015). Measuring Data Imperfection in a Neighborhood Based Method. In: Puerta, J., et al. Advances in Artificial Intelligence. CAEPIA 2015. Lecture Notes in Computer Science(), vol 9422. Springer, Cham. https://doi.org/10.1007/978-3-319-24598-0_19

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  • DOI: https://doi.org/10.1007/978-3-319-24598-0_19

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-24597-3

  • Online ISBN: 978-3-319-24598-0

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