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Interpretability of Computational Intelligence-Based Regression Models

  • Tamás Kenesei
  • János Abonyi

Part of the SpringerBriefs in Computer Science book series (BRIEFSCOMPUTER)

Table of contents

  1. Front Matter
    Pages i-x
  2. Tamás Kenesei, János Abonyi
    Pages 1-8
  3. Tamás Kenesei, János Abonyi
    Pages 9-32
  4. Tamás Kenesei, János Abonyi
    Pages 33-48
  5. Tamás Kenesei, János Abonyi
    Pages 49-60
  6. Tamás Kenesei, János Abonyi
    Pages 61-63
  7. Back Matter
    Pages 65-82

About this book

Introduction

The key idea of this book is that hinging hyperplanes, neural networks and support vector machines can be transformed into fuzzy models, and interpretability of the resulting rule-based systems can be ensured by special model reduction and visualization techniques. The first part of the book deals with the identification of hinging hyperplane-based regression trees. The next part deals with the validation, visualization and structural reduction of neural networks based on the transformation of the hidden layer of the network into an additive fuzzy rule base system. Finally, based on the analogy of support vector regression and fuzzy models, a three-step model reduction algorithm is proposed to get interpretable fuzzy regression models on the basis of support vector regression.

 

The authors demonstrate real-world use of the algorithms with examples taken from process engineering, and they support the text with downloadable Matlab code. The book is suitable for researchers, graduate students and practitioners in the areas of computational intelligence and machine learning.

Keywords

Fuzzy Logic Fuzzy c-Regression Clustering Hinging Hyperplanes Model Interpretability Model Predictive Control Model Reduction Neural Networks Non-linear Regression Recommender Systems Regression Trees Support Vector Regression Visualisation

Authors and affiliations

  • Tamás Kenesei
    • 1
  • János Abonyi
    • 2
  1. 1.Dept. of Process EngineeringUniversity of PannoniaVeszprémHungary
  2. 2.Dept. of Process EngineeringUniversity of PannoniaVeszprémHungary

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-319-21942-4
  • Copyright Information The Author(s) 2015
  • Publisher Name Springer, Cham
  • eBook Packages Computer Science
  • Print ISBN 978-3-319-21941-7
  • Online ISBN 978-3-319-21942-4
  • Series Print ISSN 2191-5768
  • Series Online ISSN 2191-5776
  • Buy this book on publisher's site
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