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Top–Down Sparse Fuzzy Regression Modeling from Data with Improved Coverage

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Abstract

We propose a new fuzzy modeling algorithm from data for regression problems. It acts in a top–down manner by allowing the user to specify an upper number of allowed rules in the rule base which is sparsed out with the usage of an iterative constrained numerical optimization procedure. It is based on the combination of the least squares error and the sum of rule weights over all rules to achieve minimal error with lowest possible number of significantly active rules. Two major novel concepts are integrated into the optimization process: the first respects a minimal coverage degree of the sample space in order to approach \(\epsilon \)-completeness of the rule base (an important interpretability criterion) and the second optimizes the positioning and ranges of influence of the rules, which is done synchronously to the optimization of the rule weights within an intervened, homogeneous procedure. Based on empirical results achieved for several high-dimensional (partially noisy) data sets, it can be shown that our advanced, intervened optimization yields fuzzy systems with a better coverage and a higher degree of \(\epsilon \)-completeness compared to the fuzzy models achieved by related data-driven fuzzy modeling methods. This is even achieved with a significantly lower or at least equal number of rules and with a similar model error on separate validation data.

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Notes

  1. http://archive.ics.uci.edu/ml/.

  2. http://eigentaste.berkeley.edu/dataset/.

  3. http://www.dcsc.tudelft.nl/~rbabuska/fmid-v40.zip.

  4. http://www.dcsc.tudelft.nl/~rbabuska/fmid-v40.zip.

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Acknowledgements

The first author acknowledges the support of the Austrian COMET-K2 programme of the Linz Center of Mechatronics (LCM), funded by the Austrian federal government and the federal state of Upper Austria, and the support of the COMET Project ’Heuristic Optimization in Production and Logistics’ (HOPL), #843532 funded by the Austrian Research Promotion Agency (FFG). This publication reflects only the authors’ views.

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Authors and Affiliations

  1. Department of Knowledge-Based Mathematical Systems, Johannes Kepler University Linz, Linz, Austria

    Edwin Lughofer

  2. Industrial Mathematics Institute, Johannes Kepler University Linz, Linz, Austria

    Stefan Kindermann

  3. Latrobe University, Melbourne, Victoria, Australia

    Mahardhika Pratama

  4. Seccion de Estudios de Posgrado e Investigacion, Esime Azcapotzalco, Instituto Politecnico Nacional, Av. de las Granjas, Mexico City, Mexico

    Jose de Jesus Rubio

Authors
  1. Edwin Lughofer
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  2. Stefan Kindermann
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  3. Mahardhika Pratama
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  4. Jose de Jesus Rubio
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Correspondence to Edwin Lughofer.

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Lughofer, E., Kindermann, S., Pratama, M. et al. Top–Down Sparse Fuzzy Regression Modeling from Data with Improved Coverage. Int. J. Fuzzy Syst. 19, 1645–1658 (2017). https://doi.org/10.1007/s40815-016-0271-0

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  • DOI: https://doi.org/10.1007/s40815-016-0271-0

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