Continuous Endpoint Data Mining with ExSTraCS: A Supervised Learning Classifier System

@article{Urbanowicz2015ContinuousED,
  title={Continuous Endpoint Data Mining with ExSTraCS: A Supervised Learning Classifier System},
  author={Ryan J. Urbanowicz and Niranjan Ramanand and Jason H. Moore},
  journal={Proceedings of the Companion Publication of the 2015 Annual Conference on Genetic and Evolutionary Computation},
  year={2015}
}
ExSTraCS is a powerful Michigan-style learning classifier system (LCS) that was developed for classification, prediction, modeling, and knowledge discovery in complex and/or heterogeneous supervised learning problems with clean or noisy signals. To date, ExSTraCS has been limited to problems with discrete endpoints (i.e. classes). Many real world problems, however, involve endpoints with continuous values (e.g. function approximation, or quantitative trait analyses). In some problems the goal… 
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