© 2011

Fusion Methods for Unsupervised Learning Ensembles


Part of the Studies in Computational Intelligence book series (SCI, volume 322)

Table of contents

  1. Front Matter
  2. Bruno Baruque, Emilio Corchado
    Pages 1-4
  3. Bruno Baruque, Emilio Corchado
    Pages 5-29
  4. Bruno Baruque, Emilio Corchado
    Pages 31-47
  5. Bruno Baruque, Emilio Corchado
    Pages 49-66
  6. Bruno Baruque, Emilio Corchado
    Pages 67-94
  7. Bruno Baruque, Emilio Corchado
    Pages 95-122
  8. Bruno Baruque, Emilio Corchado
    Pages 123-125
  9. Back Matter

About this book


The application of a “committee of experts” or ensemble learning to artificial neural networks that apply unsupervised learning techniques is widely considered to enhance the effectiveness of such networks greatly. This book examines the potential of the ensemble meta-algorithm by describing and testing a technique based on the combination of ensembles and statistical PCA that is able to determine the presence of outliers in high-dimensional data sets and to minimize outlier effects in the final results. Its central contribution concerns an algorithm for the ensemble fusion of topology-preserving maps, referred to as Weighted Voting Superposition (WeVoS), which has been devised to improve data exploration by 2-D visualization over multi-dimensional data sets. This generic algorithm is applied in combination with several other models taken from the family of topology preserving maps, such as the SOM, ViSOM, SIM and Max-SIM. A range of quality measures for topologypreserving maps that are proposed in the literature are used to validate and compare WeVoS with other algorithms. The experimental results demonstrate that, in the majority of cases, the WeVoS algorithm outperforms earlier map-fusion methods and the simpler versions of the algorithm with which it is compared. All the algorithms are tested in different artificial data sets and in several of the most common machine-learning data sets in order to corroborate their theoretical properties. Moreover, a real-life case-study taken from the food industry demonstrates the practical benefits of their application to more complex problems.


Artificial Neural Networks Computational Intelligence Ensemble Learning Fusion Methods Unsupervised Learning

Authors and affiliations

  1. 1.Departamento de Ingeniería Civil Escuela Politécnica SuperiorUniversidad de BurgosBurgosSpain
  2. 2.Departamento de Informática y Automática, Facultad de CienciasUniversidad de SalamancaSalamancaSpain

Bibliographic information

  • Book Title Fusion Methods for Unsupervised Learning Ensembles
  • Authors Bruno Baruque
  • Series Title Studies in Computational Intelligence
  • DOI
  • Copyright Information Springer Berlin Heidelberg 2011
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Engineering Engineering (R0)
  • Hardcover ISBN 978-3-642-16204-6
  • Softcover ISBN 978-3-642-42328-4
  • eBook ISBN 978-3-642-16205-3
  • Series ISSN 1860-949X
  • Series E-ISSN 1860-9503
  • Edition Number 1
  • Number of Pages XVII, 141
  • Number of Illustrations 0 b/w illustrations, 0 illustrations in colour
  • Topics Computational Intelligence
    Artificial Intelligence
  • Buy this book on publisher's site
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