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Rule Extraction using Genetic Programming for Accurate Sales Forecasting
University of Borås, School of Business and IT. (CSL@BS)
University of Borås, School of Business and IT. (CSL@BS)
2014 (English)Conference paper (Refereed)
Abstract [en]

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 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.

Place, publisher, year, edition, pages
IEEE , 2014.
Keyword [en]
Genetic programming, Rule extraction, Overfitting, Regression, Sales forecasting, Machine learning, Data mining
National Category
Computer Science Computer and Information Science
URN: urn:nbn:se:hb:diva-7320Local ID: 2320/14624ISBN: 978-1-4799-4518-4/14OAI: diva2:888033
5th IEEE Symposium on Computational Intelligence and Data Mining (CIDM 2014), 9-12 december, Orlando, FL, USA


This work was supported by the Swedish Retail and Wholesale Development

Council through the project Innovative Business Intelligence Tools (2013:5).

Available from: 2015-12-22 Created: 2015-12-22

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König, RikardJohansson, Ulf
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