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Rule extraction using genetic programming for accurate sales forecasting
2014
2014 IEEE Symposium on Computational Intelligence and Data Mining (CIDM)
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 optimization 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 labeled training
doi:10.1109/cidm.2014.7008669
dblp:conf/cidm/KonigJ14
fatcat:lrn23vyzjnen7gjvs7irop347u