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International Conference on Swarm Intelligence

ANTS 2016: Swarm Intelligence pp 137–148Cite as

Monotonicity in Ant Colony Classification Algorithms

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9882))

Abstract

Classification algorithms generally do not use existing domain knowledge during model construction. The creation of models that conflict with existing knowledge can reduce model acceptance, as users have to trust the models they use. Domain knowledge can be integrated into algorithms using semantic constraints to guide model construction. This paper proposes an extension to an existing ACO-based classification rule learner to create lists of monotonic classification rules. The proposed algorithm was compared to a majority classifier and the Ordinal Learning Model (OLM) monotonic learner. Our results show that the proposed algorithm successfully outperformed OLM’s predictive accuracy while still producing monotonic models.

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Notes

  1. 1.

    An example is covered by a rule when it satisfies all terms (attribute-value conditions) in the antecedent of the rule.

  2. 2.

    ACO-based algorithms therefore run a total of 50 times before the average is taken.

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Author information

Authors and Affiliations

  1. School of Computing, University of Kent, Chatham Maritime, UK

    James Brookhouse & Fernando E. B. Otero

Authors
  1. James Brookhouse
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  2. Fernando E. B. Otero
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Corresponding author

Correspondence to James Brookhouse .

Editor information

Editors and Affiliations

  1. Université Libre de Bruxelles , Bruxelles, Belgium

    Marco Dorigo

  2. Université Libre de Bruxelles , Bruxelles, Belgium

    Mauro Birattari

  3. RMIT University , Melbourne, Victoria, Australia

    Xiaodong Li

  4. University of Manchester, Manchester, United Kingdom

    Manuel López-Ibáñez

  5. Hiroshima University , Hiroshima, Japan

    Kazuhiro Ohkura

  6. École Polytechnique de Montréal , Montréal, Québec, Canada

    Carlo Pinciroli

  7. Université Libre de Bruxelles IRIDIA, CoDE, Brüssel, Belgium

    Thomas Stützle

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© 2016 Springer International Publishing Switzerland

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Brookhouse, J., Otero, F.E.B. (2016). Monotonicity in Ant Colony Classification Algorithms. In: Dorigo, M., et al. Swarm Intelligence. ANTS 2016. Lecture Notes in Computer Science(), vol 9882. Springer, Cham. https://doi.org/10.1007/978-3-319-44427-7_12

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  • DOI: https://doi.org/10.1007/978-3-319-44427-7_12

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-44426-0

  • Online ISBN: 978-3-319-44427-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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