Abstract
In the paper we present extensive experiments to evaluate our recently proposed method applying the ensembles of genetic fuzzy systems to build reliable predictive models from a data stream of real estate transactions. The method relies on building models over the chunks of a data stream determined by a sliding time window and incrementally expanding an ensemble by systematically generated models in the course of time. The aged models are utilized to compose ensembles and their output is updated with trend functions reflecting the changes of prices in the market. The experiments aimed at examining the impact of the number of aged models used to compose an ensemble on the accuracy and the influence of degree of polynomial trend functions applied to modify the results on the performance of single models and ensembles. The analysis of experimental results was made employing statistical approach including nonparametric tests followed by post-hoc procedures devised for multiple N×N comparisons.
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Trawiński, B., Smętek, M., Lasota, T., Trawiński, G. (2014). Evaluation of Fuzzy System Ensemble Approach to Predict from a Data Stream. In: Nguyen, N.T., Attachoo, B., Trawiński, B., Somboonviwat, K. (eds) Intelligent Information and Database Systems. ACIIDS 2014. Lecture Notes in Computer Science(), vol 8398. Springer, Cham. https://doi.org/10.1007/978-3-319-05458-2_15
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DOI: https://doi.org/10.1007/978-3-319-05458-2_15
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-05457-5
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