Hybrid Classifiers

Methods of Data, Knowledge, and Classifier Combination

  • Michal Wozniak

Part of the Studies in Computational Intelligence book series (SCI, volume 519)

Table of contents

  1. Front Matter
    Pages 1-12
  2. Michał Woźniak
    Pages 1-57
  3. Michał Woźniak
    Pages 59-93
  4. Michał Woźniak
    Pages 95-140
  5. Michał Woźniak
    Pages 141-177
  6. Michał Woźniak
    Pages 179-180
  7. Back Matter
    Pages 181-217

About this book

Introduction

This book delivers a definite and compact knowledge on how hybridization can help improving the quality of computer classification systems. In order to make readers clearly realize the knowledge of hybridization, this book primarily focuses on introducing the different levels of hybridization and illuminating what problems we will face with as dealing with such projects. In the first instance the data and knowledge incorporated in hybridization were the action points, and then a still growing up area of classifier systems known as combined classifiers was considered. This book comprises the aforementioned state-of-the-art topics and the latest research results of the author and his team from Department of Systems and Computer Networks, Wroclaw University of Technology, including as classifier based on feature space splitting, one-class classification, imbalance data, and data stream classification.

Keywords

Classifier Fusion Computational Intelligence Data Fusion Intelligent Systems

Authors and affiliations

  • Michal Wozniak
    • 1
  1. 1.Department of Systems and Computer NetworksWroclaw University of TechnologyWroclawPoland

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-642-40997-4
  • Copyright Information Springer-Verlag Berlin Heidelberg 2014
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Engineering
  • Print ISBN 978-3-642-40996-7
  • Online ISBN 978-3-642-40997-4
  • Series Print ISSN 1860-949X
  • Series Online ISSN 1860-9503
  • About this book