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Advances in Intelligent Data Analysis V

5th International Symposium on Intelligent Data Analysis, IDA 2003, Berlin, Germany, August 28-30, 2003, Proceedings

  • Conference proceedings
  • © 2003

Overview

Editors:
  1. Michael R. Berthold

    1. Berkeley Initiative in Soft Computing (BISC), University of California at Berkeley, USA

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  2. Hans-Joachim Lenz

    1. Freie Universität Berlin, Berlin, Germany

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  3. Elizabeth Bradley

    1. Department of Computer Science, University of Colorado, Boulder, Colorado, USA

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  4. Rudolf Kruse

    1. Otto-von-Guericke-University of Magdeburg, Germany

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  5. Christian Borgelt

    1. Department of Knowledge Processing and Language Engineering, University of Magdeburg, Magdeburg, Germany

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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 2810)

Included in the following conference series:

Conference proceedings info: IDA 2003.

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About this book

We are glad to present the proceedings of the 5th biennial conference in the Intelligent Data Analysis series. The conference took place in Berlin, Germany, August 28–30, 2003. IDA has by now clearly grown up. Started as a small si- symposium of a larger conference in 1995 in Baden-Baden (Germany) it quickly attractedmoreinterest(bothsubmission-andattendance-wise),andmovedfrom London (1997) to Amsterdam (1999), and two years ago to Lisbon. Submission ratesalongwiththeeverimprovingqualityofpapershaveenabledtheor- nizers to assemble increasingly consistent and high-quality programs. This year we were again overwhelmed by yet another record-breaking submission rate of 180 papers. At the Program Chairs meeting we were – based on roughly 500 reviews – in the lucky position of carefully selecting 17 papers for oral and 42 for poster presentation. Poster presenters were given the opportunity to summarize their papers in 3-minute spotlight presentations. The oral, spotlight and poster presentations were then scheduled in a single-track, 2. 5-day conference program, summarized in this book. In accordance with the goal of IDA, “to bring together researchers from diverse disciplines,” we achieved a nice balance of presentations from the more theoreticalside(bothstatisticsandcomputerscience)aswellasmoreapplicati- oriented areas that illustrate how these techniques can be used in practice. Work presented in these proceedings ranges from theoretical contributions dealing, for example, with data cleaning and compression all the way to papers addressing practical problems in the areas of text classi?cation and sales-rate predictions. A considerable number of papers also center around the currently so popular applications in bioinformatics.

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Table of contents (56 papers)

  1. Front Matter

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  2. Machine Learning

    1. Pruning for Monotone Classification Trees

      • Ad Feelders, Martijn Pardoel
      Pages 1-12

    2. Regularized Learning with Flexible Constraints

      • Eyke Hüllermeier
      Pages 13-24

    3. Learning to Answer Emails

      • Michael Kockelkorn, Andreas Lüneburg, Tobias Scheffer
      Pages 25-35

    4. A Semi-supervised Method for Learning the Structure of Robot Environment Interactions

      • Axel Großmann, Matthias Wendt, Jeremy Wyatt
      Pages 36-47

    5. Using Domain Specific Knowledge for Automated Modeling

      • Ljupčo Todorovski, Sašo Džeroski
      Pages 48-59

    6. Resolving Rule Conflicts with Double Induction

      • Tony Lindgren, Henrik Boström
      Pages 60-67

    7. A Novel Partial-Memory Learning Algorithm Based on Grey Relational Structure

      • Chi-Chun Huang, Hahn-Ming Lee
      Pages 68-75

    8. Constructing Hierarchical Rule Systems

      • Thomas R. Gabriel, Michael R. Berthold
      Pages 76-87

    9. Text Categorization Using Hybrid Multiple Model Schemes

      • In-Cheol Kim, Soon-Hee Myoung
      Pages 88-99

  3. Probability and Topology

    1. Learning Dynamic Bayesian Networks from Multivariate Time Series with Changing Dependencies

      • Allan Tucker, Xiaohui Liu
      Pages 100-110

    2. Topology and Intelligent Data Analysis

      • V. Robins, J. Abernethy, N. Rooney, E. Bradley
      Pages 111-122

    3. Coherent Conditional Probability as a Measure of Information of the Relevant Conditioning Events

      • Giulianella Coletti, Romano Scozzafava
      Pages 123-133

    4. Very Predictive Ngrams for Space-Limited Probabilistic Models

      • Paul R. Cohen, Charles A. Sutton
      Pages 134-142

    5. Interval Estimation Naïve Bayes

      • V. Robles, P. Larrañaga, J. M. Peña, E. Menasalvas, M. S. Pérez
      Pages 143-154

    6. Mining Networks and Central Entities in Digital Libraries. A Graph Theoretic Approach Applied to Co-author Networks

      • Peter Mutschke
      Pages 155-166

  4. Classification and Pattern Recognition

    1. Learning Linear Classifiers Sensitive to Example Dependent and Noisy Costs

      • Peter Geibel, Ulf Brefeld, Fritz Wysotzki
      Pages 167-178

    2. An Effective Associative Memory for Pattern Recognition

      • Boris Kryzhanovsky, Leonid Litinskii, Anatoly Fonarev
      Pages 179-186

    3. Similarity Based Classification

      • Axel E. Bernal, Karen Hospevian, Tayfun Karadeniz, Jean-Louis Lassez
      Pages 187-197

    4. Numerical Attributes in Decision Trees: A Hierarchical Approach

      • Fernando Berzal, Juan-Carlos Cubero, Nicolás Marín, Daniel Sánchez
      Pages 198-207
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Other volumes

  1. Advances in Intelligent Data Analysis V

Editors and Affiliations

  • Berkeley Initiative in Soft Computing (BISC), University of California at Berkeley, USA

    Michael R. Berthold

  • Freie Universität Berlin, Berlin, Germany

    Hans-Joachim Lenz

  • Department of Computer Science, University of Colorado, Boulder, Colorado, USA

    Elizabeth Bradley

  • Otto-von-Guericke-University of Magdeburg, Germany

    Rudolf Kruse

  • Department of Knowledge Processing and Language Engineering, University of Magdeburg, Magdeburg, Germany

    Christian Borgelt

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