Ensembles in Machine Learning Applications

  • Oleg Okun
  • Giorgio Valentini
  • Matteo Re

Part of the Studies in Computational Intelligence book series (SCI, volume 373)

Table of contents

  1. Front Matter
  2. Miguel Ángel Bautista, Sergio Escalera, Xavier Baró, Oriol Pujol, Jordi Vitrià, Petia Radeva
    Pages 21-38
  3. Evgueni N. Smirnov, Matthijs Moed, Georgi Nalbantov, Ida Sprinkhuizen-Kuyper
    Pages 39-58
  4. Cemre Zor, Terry Windeatt, Berrin Yanikoglu
    Pages 59-73
  5. Benjamin Schowe, Katharina Morik
    Pages 75-95
  6. Rakkrit Duangsoithong, Terry Windeatt
    Pages 97-115
  7. Houtao Deng, Saylisse Davila, George Runger, Eugene Tuv
    Pages 117-131
  8. Stefano Ceccon, David Garway-Heath, David Crabb, Allan Tucker
    Pages 133-150
  9. Alessandro Rozza, Gabriele Lombardi, Matteo Re, Elena Casiraghi, Giorgio Valentini, Paola Campadelli
    Pages 151-167
  10. Haytham Elghazel, Alex Aussem, Florence Perraud
    Pages 169-179
  11. Carlos Pardo, Juan J. Rodríguez, José F. Díez-Pastor, César García-Osorio
    Pages 181-199
  12. Pierluigi Casale, Oriol Pujol, Petia Radeva
    Pages 201-216
  13. Back Matter

About this book

Introduction

This book contains the extended papers presented at the 3rd Workshop on Supervised and Unsupervised Ensemble Methods
and their Applications (SUEMA) that was held in conjunction with the European Conference on Machine Learning and
Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD 2010, Barcelona, Catalonia, Spain).
As its two predecessors, its main theme was ensembles of supervised and unsupervised algorithms – advanced machine
learning and data mining technique. Unlike a single classification or clustering algorithm, an ensemble is a group
of algorithms, each of which first independently solves the task at hand by assigning a class or cluster label
(voting) to instances in a dataset and after that all votes are combined together to produce the final class or
cluster membership. As a result, ensembles often outperform best single algorithms in many real-world problems.
 
This book consists of 14 chapters, each of which can be read independently of the others. In addition to two
previous SUEMA editions, also published by Springer, many chapters in the current book include pseudo code and/or
programming code of the algorithms described in them. This was done in order to facilitate ensemble adoption in
practice and to help to both researchers and engineers developing ensemble applications.

Keywords

Computational Intelligence Computational Intelligence Ensembles in Machine Learning Applications Ensembles in Machine Learning Applications Machine Learning Machine Learning

Editors and affiliations

  • Oleg Okun
    • 1
  • Giorgio Valentini
    • 2
  • Matteo Re
    • 3
  1. 1.University of MalmoMalmöSweden
  2. 2.Department of Computer ScienceUniversity of MilanMilanoItaly
  3. 3.Department of Computer ScienceUniversity of MilanMilanoItalia

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-642-22910-7
  • Copyright Information Springer Berlin Heidelberg 2011
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Engineering
  • Print ISBN 978-3-642-22909-1
  • Online ISBN 978-3-642-22910-7
  • Series Print ISSN 1860-949X
  • Series Online ISSN 1860-9503
  • About this book
Industry Sectors
Pharma
Automotive
Chemical Manufacturing
Biotechnology
Electronics
Telecommunications
Energy, Utilities & Environment
Aerospace
Oil, Gas & Geosciences