Learning in Non-Stationary Environments

Methods and Applications

  • Moamar Sayed-Mouchaweh
  • Edwin Lughofer

Table of contents

  1. Front Matter
    Pages i-xii
  2. Moamar Sayed-Mouchaweh, Edwin Lughofer
    Pages 1-17
  3. Dynamic Methods for Unsupervised Learning Problems

    1. Front Matter
      Pages 19-19
    2. Katharina Tschumitschew, Frank Klawonn
      Pages 21-55
    3. Witold Pedrycz, John Berezowski, Iqbal Jamal
      Pages 57-75
    4. Abdelhamid Bouchachia, Markus Prossegger
      Pages 77-99
  4. Dynamic Methods for Supervised Classification Problems

    1. Front Matter
      Pages 101-101
    2. Laurent Hartert, Moamar Sayed-Mouchaweh
      Pages 103-124
    3. James Edward Smith, Muhammad Atif Tahir, Davy Sannen, Hendrik Van Brussel
      Pages 125-151
    4. Davy Sannen, Jean-Michel Papy, Steve Vandenplas, Edwin Lughofer, Hendrik Van Brussel
      Pages 153-184
    5. Ammar Shaker, Eyke Hüllermeier
      Pages 185-201
  5. Dynamic Methods for Supervised Regression Problems

    1. Front Matter
      Pages 203-203
    2. Daniel Leite, Pyramo Costa, Fernando Gomide
      Pages 271-300
  6. Applications of Learning in Non-Stationary Environments

    1. Front Matter
      Pages 301-301
    2. Harya Widiputra, Russel Pears, Nikola Kasabov
      Pages 303-347
    3. Christian Eitzinger, Stefan Thumfart
      Pages 349-374
    4. Edwin Lughofer, Christian Eitzinger, Carlos Guardiola
      Pages 375-406
    5. Moamar Sayed-Mouchaweh, Nadhir Messai, Omar Ayad, Sofiane Mazeghrane
      Pages 407-427
  7. Back Matter
    Pages 429-440

About this book


Recent decades have seen rapid advances in automatization processes, supported by modern machines and computers. The result is significant increases in system complexity and state changes, information sources, the need for faster data handling and the integration of environmental influences. Intelligent systems, equipped with a taxonomy of data-driven system identification and machine learning algorithms, can handle these problems partially. Conventional learning algorithms in a batch off-line setting fail whenever dynamic changes of the process appear due to non-stationary environments and external influences.


Learning in Non-Stationary Environments: Methods and Applications offers a wide-ranging, comprehensive review of recent developments and important methodologies in the field. The coverage focuses on dynamic learning in unsupervised problems, dynamic learning in supervised classification and dynamic learning in supervised regression problems. A later section is dedicated to applications in which dynamic learning methods serve as keystones for achieving models with high accuracy.


Rather than rely on a mathematical theorem/proof style, the editors highlight numerous figures, tables, examples and applications, together with their explanations.


This approach offers a useful basis for further investigation and fresh ideas and motivates and inspires newcomers to explore this promising and still emerging field of research.



Dynamic learning Knowledge extraction adaptive modeling data streams drifts and shifts dynamic dimension reduction huge data bases incremental learning on-line industrial applications on-line modeling

Editors and affiliations

  • Moamar Sayed-Mouchaweh
    • 1
  • Edwin Lughofer
    • 2
  1. 1., Départment Informatique et AutomatiqueEcole des Mines de DouaiDouai cedexFrance
  2. 2.University of LinzLinzAustria

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