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Mining Incomplete Data with Attribute-Concept Values and “Do Not Care” Conditions

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Book cover Hybrid Artificial Intelligence Systems (HAIS 2014)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8480))

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Abstract

In this paper we present novel experimental results on comparing two interpretations of missing attribute values: attribute-concept values and “do not care” conditions. Experiments were conducted on 176 data sets, with preprocessing using three kinds of probabilistic approximations (lower, middle and upper) and the MLEM2 rule induction system. The performance was evaluated using the error rate computed by ten-fold cross validation. At 5% statistical significance level, in four cases attribute-concept values and in two cases “do not care” conditions performed better (out of 24 cases). At 10% statistical significance level, in five cases attribute-concept values and in three cases “do not care” conditions performed better. In the remaining cases the differences were not statistically significant.

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Author information

Authors and Affiliations

  1. Department of Electrical Engineering and Computer Science, University of Kansas, Lawrence, KS, 66045, USA

    Patrick G. Clark & Jerzy W. Grzymala-Busse

  2. Department of Expert Systems and Artificial Intelligence, University of Information Technology and Management, 35-225, Rzeszow, Poland

    Jerzy W. Grzymala-Busse

Authors
  1. Patrick G. Clark
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  2. Jerzy W. Grzymala-Busse
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Editor information

Editors and Affiliations

  1. Department of Electrical and Computer Engineering, University of Cyprus, 75 Kallipoleos Avenue, 1678, Nicosia, Cyprus

    Marios Polycarpou

  2. Department of Computer Science, University of Sao Paulo at Sao Carlos, Sao Carlos, SP, Brazil

    André C. P. L. F. de Carvalho

  3. Department of Electronic Engineering, National Kaohsiung University of Applied Sciences, No. 415, Chien Kung Road, 80778, Kaohsiung, Taiwan

    Jeng-Shyang Pan

  4. Wroclaw University of Technology, Wybrzeze Wyspianskiego 27, 50-370, Wroclaw, Poland

    Michał Woźniak

  5. University of Salamanca, Plaza de la Merced S/N, 37008 Salamanca, Spain and University of A Coruna, Escuela Universitaria Politecnica, Departamento de Enxeñeria Industrial, A Coruna, Spain

    Héctor Quintian

  6. University of Salamanca, Plaza de la Merced S/N, 37008, Salamanca, Spain

    Emilio Corchado

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© 2014 Springer International Publishing Switzerland

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Clark, P.G., Grzymala-Busse, J.W. (2014). Mining Incomplete Data with Attribute-Concept Values and “Do Not Care” Conditions. In: Polycarpou, M., de Carvalho, A.C.P.L.F., Pan, JS., Woźniak, M., Quintian, H., Corchado, E. (eds) Hybrid Artificial Intelligence Systems. HAIS 2014. Lecture Notes in Computer Science(), vol 8480. Springer, Cham. https://doi.org/10.1007/978-3-319-07617-1_14

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  • DOI: https://doi.org/10.1007/978-3-319-07617-1_14

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-07616-4

  • Online ISBN: 978-3-319-07617-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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