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New Classification Method Based on Modular Neural Networks with the LVQ Algorithm and Type-2 Fuzzy Logic

  • Jonathan Amezcua
  • Patricia Melin
  • Oscar Castillo

Part of the SpringerBriefs in Applied Sciences and Technology book series (BRIEFSAPPLSCIENCES)

Also part of the SpringerBriefs in Computational Intelligence book sub series (BRIEFSINTELL)

Table of contents

  1. Front Matter
    Pages i-viii
  2. Jonathan Amezcua, Patricia Melin, Oscar Castillo
    Pages 1-3
  3. Jonathan Amezcua, Patricia Melin, Oscar Castillo
    Pages 5-27
  4. Jonathan Amezcua, Patricia Melin, Oscar Castillo
    Pages 29-32
  5. Jonathan Amezcua, Patricia Melin, Oscar Castillo
    Pages 33-39
  6. Jonathan Amezcua, Patricia Melin, Oscar Castillo
    Pages 41-54
  7. Jonathan Amezcua, Patricia Melin, Oscar Castillo
    Pages 55-56
  8. Back Matter
    Pages 57-73

About this book

Introduction

In this book a new model for data classification was developed. This new model is based on the competitive neural network Learning Vector Quantization (LVQ) and type-2 fuzzy logic.  This computational model consists of the hybridization of the aforementioned techniques, using a fuzzy logic system within the competitive layer of the LVQ network to determine the shortest distance between a centroid and an input vector. This new model is based on a modular LVQ architecture to further improve its performance on complex classification problems. It also implements a data-similarity process for preprocessing the datasets, in order to build dynamic architectures, having the classes with the highest degree of similarity in different modules. Some architectures were developed in order to work mainly with two datasets, an arrhythmia dataset (using ECG signals) for classifying 15 different types of arrhythmias, and a satellite images segments dataset used for classifying six different types of soil. Both datasets show interesting features that makes them interesting for testing new classification methods.

 

Keywords

Computational Intelligence Modular Neural Networks LVQ Type-2 Fuzzy Logic Learning Vector Quantization Data classification

Authors and affiliations

  • Jonathan Amezcua
    • 1
  • Patricia Melin
    • 2
  • Oscar Castillo
    • 3
  1. 1.Division of Graduate StudiesTijuana Institute of TechnologyTijuanaMexico
  2. 2.Division of Graduate StudiesTijuana Institute of TechnologyTijuanaMexico
  3. 3.Division of Graduate StudiesTijuana Institute of TechnologyTijuanaMexico

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-319-73773-7
  • Copyright Information The Author(s) 2018
  • Publisher Name Springer, Cham
  • eBook Packages Engineering
  • Print ISBN 978-3-319-73772-0
  • Online ISBN 978-3-319-73773-7
  • Series Print ISSN 2191-530X
  • Series Online ISSN 2191-5318
  • Buy this book on publisher's site
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