On a New Method of Dynamic Integration of Fuzzy Linear Regression Models

Conference paper

DOI: 10.1007/978-3-319-59162-9_19

Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 578)
Cite this paper as:
Kozerski J., Kurzynski M. (2018) On a New Method of Dynamic Integration of Fuzzy Linear Regression Models. In: Kurzynski M., Wozniak M., Burduk R. (eds) Proceedings of the 10th International Conference on Computer Recognition Systems CORES 2017. CORES 2017. Advances in Intelligent Systems and Computing, vol 578. Springer, Cham

Abstract

In the study the problem of ensemble regression with fuzzy linear regression (FLR) models is considered. For this case a novel method of integration is proposed in which first fuzzy responses of base FLR models are integrated and next the fuzzy response of a common model is defuzzified. Four different operators are defined for integration procedure. The performance of proposed integration methods of FLR base models on the soft level were compared against state-of-the-art integration method on the crisp level using computer generated datasets with linear, 2-order and 3-order models and different variances of Gaussian disturbances. As a criterion of method quality the root mean square error was applied. The results of computer experiments clearly show that in many cases proposed methods significant outperform the reference approach.

Keywords

Fuzzy regression model Regression ensemble Integration method Fuzzy integration 

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.Wroclaw University of Science and TechnologyWroclawPoland

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