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cAnt-Miner: An Ant Colony Classification Algorithm to Cope with Continuous Attributes

  • Fernando E. B. Otero
  • Alex A. Freitas
  • Colin G. Johnson
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5217)

Abstract

This paper presents an extension to Ant-Miner, named cAnt-Miner (Ant-Miner coping with continuous attributes), which incorporates an entropy-based discretization method in order to cope with continuous attributes during the rule construction process. By having the ability to create discrete intervals for continuous attributes “on-the-fly”, cAnt-Miner does not requires a discretization method in a preprocessing step, as Ant-Miner requires. cAnt-Miner has been compared against Ant-Miner in eight public domain datasets with respect to predictive accuracy and simplicity of the discovered rules. Empirical results show that creating discrete intervals during the rule construction process facilitates the discovery of more accurate and significantly simpler classification rules.

Keywords

Continuous Attribute Discretization Method Construction Graph Discrete Interval Nominal Attribute 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Fernando E. B. Otero
    • 1
  • Alex A. Freitas
    • 1
  • Colin G. Johnson
    • 1
  1. 1.Computing LaboratoryUniversity of KentCanterburyUK

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