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Multiple Classifier Systems

5th International Workshop, MCS 2004, Cagliari, Italy, June 9-11, 2004, Proceedings

  • Conference proceedings
  • © 2004

Overview

Editors:
  1. Fabio Roli

    1. Department of Electrical and Electronic Engineering, Piazza d’Armi, University of Cagliari, Cagliari, Italy

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  2. Josef Kittler

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

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    You can also search for this editor in PubMed   Google Scholar

  3. Terry Windeatt

    1. Centre for Vision, Speech and Signal Proc (CVSSP), University of Surrey, Guildford, United Kingdom

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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 3077)

Included in the following conference series:

Conference proceedings info: MCS 2004.

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Table of contents (37 papers)

  1. Design Methods

    1. A Method for Designing Cost-Sensitive ECOC

      • Claudio Marrocco, Francesco Tortorella
      Pages 204-213

    2. Building Graph-Based Classifier Ensembles by Random Node Selection

      • Adam Schenker, Horst Bunke, Mark Last, Abraham Kandel
      Pages 214-222

    3. A Comparison of Ensemble Creation Techniques

      • Robert E. Banfield, Lawrence O. Hall, Kevin W. Bowyer, Divya Bhadoria, W. Philip Kegelmeyer, Steven Eschrich
      Pages 223-232

    4. Multiple Classifiers System for Reducing Influences of Atypical Observations

      • Šarūnas Raudys, Masakazu Iwamura
      Pages 233-242

    5. Sharing Training Patterns among Multiple Classifiers

      • Rozita Dara, Mohamed Kamel
      Pages 243-252

  2. Performance Analysis

    1. First Experiments on Ensembles of Radial Basis Functions

      • Carlos Hernández-Espinosa, Mercedes Fernández-Redondo, Joaquín Torres-Sospedra
      Pages 253-262

    2. Random Aggregated and Bagged Ensembles of SVMs: An Empirical Bias–Variance Analysis

      • Giorgio Valentini
      Pages 263-272

    3. Building Diverse Classifier Outputs to Evaluate the Behavior of Combination Methods: The Case of Two Classifiers

      • Héla Zouari, Laurent Heutte, Yves Lecourtier, Adel Alimi
      Pages 273-282

    4. An Empirical Comparison of Hierarchical vs. Two-Level Approaches to Multiclass Problems

      • Suju Rajan, Joydeep Ghosh
      Pages 283-292

    5. Experiments on Ensembles with Missing and Noisy Data

      • Prem Melville, Nishit Shah, Lilyana Mihalkova, Raymond J. Mooney
      Pages 293-302

  3. Applications

    1. Induced Decision Fusion in Automated Sign Language Interpretation: Using ICA to Isolate the Underlying Components of Sign

      • David Windridge, Richard Bowden
      Pages 303-313

    2. Ensembles of Classifiers Derived from Multiple Prototypes and Their Application to Handwriting Recognition

      • Simon Günter, Horst Bunke
      Pages 314-323

    3. Network Intrusion Detection by a Multi-stage Classification System

      • Luigi Pietro Cordella, Alessandro Limongiello, Carlo Sansone
      Pages 324-333

    4. Application of Breiman’s Random Forest to Modeling Structure-Activity Relationships of Pharmaceutical Molecules

      • Vladimir Svetnik, Andy Liaw, Christopher Tong, Ting Wang
      Pages 334-343

    5. Experimental Study on Multiple LDA Classifier Combination for High Dimensional Data Classification

      • Xiaogang Wang, Xiaoou Tang
      Pages 344-353

    6. Physics-Based Decorrelation of Image Data for Decision Level Fusion in Face Verification

      • Josef Kittler, Mohammad T. Sadeghi
      Pages 354-363

    7. High Security Fingerprint Verification by Perceptron-Based Fusion of Multiple Matchers

      • Gian Luca Marcialis, Fabio Roli
      Pages 364-373

    8. Second Guessing a Commercial’Black Box’ Classifier by an’In House’ Classifier: Serial Classifier Combination in a Speech Recognition Application

      • Fuad Rahman, Yuliya Tarnikova, Aman Kumar, Hassan Alam
      Pages 374-383

  4. Back Matter

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Keywords

About this book

The fusion of di?erent information sourcesis a persistent and intriguing issue. It hasbeenaddressedforcenturiesinvariousdisciplines,includingpoliticalscience, probability and statistics, system reliability assessment, computer science, and distributed detection in communications. Early seminal work on fusion was c- ried out by pioneers such as Laplace and von Neumann. More recently, research activities in information fusion have focused on pattern recognition. During the 1990s,classi?erfusionschemes,especiallyattheso-calleddecision-level,emerged under a plethora of di?erent names in various scienti?c communities, including machine learning, neural networks, pattern recognition, and statistics. The d- ferent nomenclatures introduced by these communities re?ected their di?erent perspectives and cultural backgrounds as well as the absence of common forums and the poor dissemination of the most important results. In 1999, the ?rst workshop on multiple classi?er systems was organized with the main goal of creating a common international forum to promote the diss- ination of the results achieved in the diverse communities and the adoption of a common terminology, thus giving the di?erent perspectives and cultural ba- grounds some concrete added value. After ?ve meetings of this workshop, there is strong evidence that signi?cant steps have been made towards this goal. - searchers from these diverse communities successfully participated in the wo- shops, and world experts presented surveys of the state of the art from the perspectives of their communities to aid cross-fertilization.

Editors and Affiliations

  • Department of Electrical and Electronic Engineering, Piazza d’Armi, University of Cagliari, Cagliari, Italy

    Fabio Roli

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

    Josef Kittler

  • Centre for Vision, Speech and Signal Proc (CVSSP), University of Surrey, Guildford, United Kingdom

    Terry Windeatt

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