Ant Colony Optimization for Feature Subset Selection

@inproceedings{AlAni2005AntCO,
  title={Ant Colony Optimization for Feature Subset Selection},
  author={Ahmed Al-Ani},
  booktitle={WEC},
  year={2005}
}
The Ant Colony Optimization (ACO) is a metaheuristic inspired by the behavior of real ants in their search for the shortest paths to food sources. It has recently attracted a lot of attention and has been successfully applied to a number of different optimization problems. Due to the importance of the feature selection problem and the potential of ACO, this paper presents a novel method that utilizes the ACO algorithm to implement a feature subset search procedure. Initial results obtained… CONTINUE READING
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Langley. Selection of relevant features and examples in machine learning

  • P.A.L. Blum
  • Artificial Intelligence,
  • 1997
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