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Classification Rule Mining with Iterated Greedy

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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 the context of data mining, classification rule discovering is the task of designing accurate rule based systems that model the useful knowledge that differentiate some data classes from others, and is present in large data sets.

Iterated greedy search is a powerful metaheuristic, successfully applied to different optimisation problems, which to our knowledge, has not previously been used for classification rule mining.

In this work, we analyse the convenience of using iterated greedy algorithms for the design of rule classification systems. We present and study different alternatives and compare the results with state-of-the-art methodologies from the literature. The results show that iterated greedy search may generate accurate rule classification systems with acceptable interpretability levels.

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Author information

Authors and Affiliations

  1. I+D Dpt., Yerbabuena Software, Málaga, 29590, España

    Juan A. Pedraza

  2. Dpt. of Computing and Numerical Analysis, University of Córdoba, Córdoba, 14071, España

    Carlos García-Martínez, Alberto Cano & Sebastián Ventura

Authors
  1. Juan A. Pedraza
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  2. Carlos García-Martínez
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  3. Alberto Cano
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  4. Sebastián Ventura
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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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Pedraza, J.A., García-Martínez, C., Cano, A., Ventura, S. (2014). Classification Rule Mining with Iterated Greedy. 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_51

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  • DOI: https://doi.org/10.1007/978-3-319-07617-1_51

  • 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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