Rule extraction using genetic programming for accurate sales forecasting

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InProceedings{Konig:2014:CIDM,
  author =       "R. Konig and U. Johansson",
  booktitle =    "IEEE Symposium on Computational Intelligence and Data
                 Mining (CIDM 2014)",
  title =        "Rule extraction using genetic programming for accurate
                 sales forecasting",
  year =         "2014",
  month =        dec,
  pages =        "210--216",
  abstract =     "The purpose of this paper is to propose and evaluate a
                 method for reducing the inherent tendency of genetic
                 programming to overfit small and noisy data sets. In
                 addition, the use of different optimisation criteria
                 for symbolic regression is demonstrated. The key idea
                 is to reduce the risk of overfitting noise in the
                 training data by introducing an intermediate predictive
                 model in the process. More specifically, instead of
                 directly evolving a genetic regression model based on
                 labelled training data, the first step is to generate a
                 highly accurate ensemble model. Since ensembles are
                 very robust, the resulting predictions will contain
                 less noise than the original data set. In the second
                 step, an interpretable model is evolved, using the
                 ensemble predictions, instead of the true labels, as
                 the target variable. Experiments on 175 sales
                 forecasting data sets, from one of Sweden's largest
                 wholesale companies, show that the proposed technique
                 obtained significantly better predictive performance,
                 compared to both straightforward use of genetic
                 programming and the standard M5P technique. Naturally,
                 the level of improvement depends critically on the
                 performance of the intermediate ensemble.",
  keywords =     "genetic algorithms, genetic programming",
  DOI =          "doi:10.1109/CIDM.2014.7008669",
  notes =        "Also known as \cite{7008669}",
}

Genetic Programming entries for Rikard Konig Ulf Johansson

Citations