Rapid Rule Compaction Strategies for Global Knowledge Discovery in a Supervised Learning Classifier System

@inproceedings{Tan2013RapidRC,
  title={Rapid Rule Compaction Strategies for Global Knowledge Discovery in a Supervised Learning Classifier System},
  author={Jie Tan and Jason H. Moore and Ryan J. Urbanowicz},
  booktitle={ECAL},
  year={2013}
}
Michigan-style learning classifier systems have availed themselves as a promising modeling and data mining strategy for bioinformaticists seeking to connect predictive variables with disease phenotypes. The resulting ‘model’ learned by these algorithms is comprised of an entire population of rules, some of which will inevitably be redundant or poor predictors. Rule compaction is a post-processing strategy for consolidating this rule population with the goal of improving interpretation and… CONTINUE READING

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