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

Authors:
Fernando E. Otero;Alex A. Freitas;Colin G. Johnson
Affiliations:
Computing Laboratory, University of Kent, Canterbury, UK;Computing Laboratory, University of Kent, Canterbury, UK;Computing Laboratory, University of Kent, Canterbury, UK
Venue:
ANTS '08 Proceedings of the 6th international conference on Ant Colony Optimization and Swarm Intelligence
Year:
2008

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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.