Instance-linked attribute tracking and feedback for michigan-style supervised learning classifier systems

@inproceedings{Urbanowicz2012InstancelinkedAT,
  title={Instance-linked attribute tracking and feedback for michigan-style supervised learning classifier systems},
  author={Ryan J. Urbanowicz and Ambrose Granizo-Mackenzie and Jason H. Moore},
  booktitle={GECCO '12},
  year={2012}
}
The application of learning classifier systems (LCSs) to classification and data mining in genetic association studies has been the target of previous work. Recent efforts have focused on: (1) correctly discriminating between predictive and non-predictive attributes, and (2) detecting and characterizing epistasis (attribute interaction) and heterogeneity. While the solutions evolved by Michigan-style LCSs (M-LCSs) are conceptually well suited to address these phenomena, the explicit… 
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