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

نویسندگان

  • Fernando E. B. Otero
  • Alex Alves Freitas
  • Colin G. Johnson
چکیده

This paper presents an extension to Ant-Miner, named cAntMiner (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 AntMiner 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.

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تاریخ انتشار 2008