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Multiple Classifier Systems

Second International Workshop, MCS 2001 Cambridge, UK, July 2-4, 2001 Proceedings

  • Conference proceedings
  • © 2001

Overview

Editors:
  1. Josef Kittler

    1. Centre for Vision, Speech and Signal Processing, University of Surrey, Guildford, UK

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  2. Fabio Roli

    1. Department of Electrical and Electronic Engineering, University of Cagliari, Cagliari, Italy

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    You can also search for this editor in PubMed   Google Scholar

Part of the book series: Lecture Notes in Computer Science (LNCS, volume 2096)

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Table of contents (44 papers)

  1. Ensemble Classifiers

    1. A Multi-SVM Classification System

      • Dimitrios S. Frossyniotis, Andreas Stafylopatis
      Pages 198-207

    2. Automatic Classification of Clustered Microcalcifications by a Multiple Classifier System

      • P. Foggia, C. Sansone, F. Tortorella, M. Vento
      Pages 208-217

  2. Feature Spaces for MCS

    1. Feature Weighted Ensemble Classifiers – A Modified Decision Scheme

      • Thomas Martini Jørgensen, Christian Linneberg
      Pages 218-227

    2. Feature Subsets for Classifier Combination: An Enumerative Experiment

      • Ludmila I. Kuncheva, Christopher J. Whitaker
      Pages 228-237

    3. Input Decimation Ensembles: Decorrelation through Dimensionality Reduction

      • Nikunj C. Oza, Kagan Tumer
      Pages 238-247

    4. Classifier Combination as a Tomographic Process

      • D. Windridge, J. Kittler
      Pages 248-258

  3. MCS in Remote Sensing

    1. A Robust Multiple Classifier System for a Partially Unsupervised Updating of Land-Cover Maps

      • Lorenzo Bruzzone, Roberto Cossu
      Pages 259-268

    2. Combining Supervised Remote Sensing Image Classifiers Based on Individual Class Performances

      • Paul C. Smits
      Pages 269-278

    3. Boosting, Bagging, and Consensus Based Classification of Multisource Remote Sensing Data

      • Gunnar Jakob Briem, Jon Atli Benediktsson, Johannes R. Sveinsson
      Pages 279-288

    4. Solar Wind Data Analysis Using Self-Organizing Hierarchical Neural Network Classifiers

      • S. A. Dolenko, Y. V. Orlov, I. G. Persiantsev, J. S. Shugai, A. V. Dmitriev, A. V. Suvorova et al.
      Pages 289-298

  4. One Class MCS and Clustering

    1. Combining One-Class Classifiers

      • David M. J. Tax, Robert P. W. Duin
      Pages 299-308

    2. Finding Consistent Clusters in Data Partitions

      • Ana Fred
      Pages 309-318

    3. A Self-Organising Approach to Multiple Classifier Fusion

      • S. P. Luttrell
      Pages 319-328

  5. Combination Strategies

    1. Error Rejection in Linearly Combined Multiple Classifiers

      • Giorgio Fumera, Fabio Roli
      Pages 329-338

    2. Relationship of Sum and Vote Fusion Strategies

      • J. Kittler, F. M. Alkoot
      Pages 339-348

    3. Complexity of Data Subsets Generated by the Random Subspace Method: An Experimental Investigation

      • L. I. Kuncheva, F. Roli, G. L. Marcialis, C. A. Shipp
      Pages 349-358

    4. On Combining Dissimilarity Representations

      • Elżbieta PÄ™kalska, Robert P. W. Duin
      Pages 359-368

    5. Application of Multiple Classifier Techniques to Subband Speaker Identification with an HMM/ANN System

      • J. E. Higgins, T. J. Dodd, R. I. Damper
      Pages 369-377

    6. Classification of Time Series Utilizing Temporal and Decision Fusion

      • C. Dietrich, F. Schwenker, G. Palm
      Pages 378-387

    7. Use of Positional Information in Sequence Alignment for Multiple Classifier Combination

      • U. -V. Marti, H. Bunke
      Pages 388-398
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Keywords

About this book

Driven by the requirements of a large number of practical and commercially - portant applications, the last decade has witnessed considerable advances in p- tern recognition. Better understanding of the design issues and new paradigms, such as the Support Vector Machine, have contributed to the development of - proved methods of pattern classi cation. However, while any performance gains are welcome, and often extremely signi cant from the practical point of view, it is increasingly more challenging to reach the point of perfection as de ned by the theoretical optimality of decision making in a given decision framework. The asymptoticity of gains that can be made for a single classi er is a re?- tion of the fact that any particular design, regardless of how good it is, simply provides just one estimate of the optimal decision rule. This observation has motivated the recent interest in Multiple Classi er Systems , which aim to make use of several designs jointly to obtain a better estimate of the optimal decision boundary and thus improve the system performance. This volume contains the proceedings of the international workshop on Multiple Classi er Systems held at Robinson College, Cambridge, United Kingdom (July 2{4, 2001), which was organized to provide a forum for researchers in this subject area to exchange views and report their latest results.

Editors and Affiliations

  • Centre for Vision, Speech and Signal Processing, University of Surrey, Guildford, UK

    Josef Kittler

  • Department of Electrical and Electronic Engineering, University of Cagliari, Cagliari, Italy

    Fabio Roli

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