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Machine Learning and Data Mining in Pattern Recognition

6th International Conference, MLDM 2009, Leipzig, Germany, July 23-25, 2009, Proceedings

  • Conference proceedings
  • © 2009

Overview

Editors:
  1. Petra Perner

    1. Institut für Bildverarbeitung und angewandte Informatik, Leipzig, Germany

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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 5632)

Part of the book sub series: Lecture Notes in Artificial Intelligence (LNAI)

Included in the following conference series:

Conference proceedings info: MLDM 2009.

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About this book

There is no royal road to science, and only those who do not dread the fatiguing climb of its steep paths have a chance of gaining its luminous summits. Karl Marx A Universial Genius of the 19th Century Many scientists from all over the world during the past two years since the MLDM 2007 have come along on the stony way to the sunny summit of science and have worked hard on new ideas and applications in the area of data mining in pattern r- ognition. Our thanks go to all those who took part in this year's MLDM. We appre- ate their submissions and the ideas shared with the Program Committee. We received over 205 submissions from all over the world to the International Conference on - chine Learning and Data Mining, MLDM 2009. The Program Committee carefully selected the best papers for this year’s program and gave detailed comments on each submitted paper. There were 63 papers selected for oral presentation and 17 papers for poster presentation. The topics range from theoretical topics for classification, clustering, association rule and pattern mining to specific data-mining methods for the different multimedia data types such as image mining, text mining, video mining and Web mining. Among these topics this year were special contributions to subtopics such as attribute discre- zation and data preparation, novelty and outlier detection, and distances and simila- ties.

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Table of contents (61 papers)

  1. Front Matter

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  2. Attribute Discretization and Data Preparation

    1. Improved Comprehensibility and Reliability of Explanations via Restricted Halfspace Discretization

      • Klaus Truemper
      Pages 1-15

    2. Selection of Subsets of Ordered Features in Machine Learning

      • O. Seredin, A. Kopylov, V. Mottl
      Pages 16-28

    3. Combination of Vector Quantization and Visualization

      • Olga Kurasova, Alma Molytė
      Pages 29-43

    4. Discretization of Target Attributes for Subgroup Discovery

      • Katherine Moreland, Klaus Truemper
      Pages 44-52

    5. Preserving Privacy in Time Series Data Classification by Discretization

      • Ye Zhu, Yongjian Fu, Huirong Fu
      Pages 53-67

    6. Using Resampling Techniques for Better Quality Discretization

      • Taimur Qureshi, Djamel A. Zighed
      Pages 68-81

  3. Classification

    1. A Large Margin Classifier with Additional Features

      • Xinwang Liu, Jianping Yin, En Zhu, Guomin Zhang, Yubin Zhan, Miaomiao Li
      Pages 82-95

    2. Sequential EM for Unsupervised Adaptive Gaussian Mixture Model Based Classifier

      • Bashar Awwad Shiekh Hasan, John Q. Gan
      Pages 96-106

    3. Optimal Double-Kernel Combination for Classification

      • Feng Wang, Hongbin Zhang
      Pages 107-122

    4. Efficient AdaBoost Region Classification

      • M. Moed, E. N. Smirnov
      Pages 123-136

    5. A Linear Classification Method in a Very High Dimensional Space Using Distributed Representation

      • Takao Kobayashi, Ikuko Shimizu
      Pages 137-147

    6. PMCRI: A Parallel Modular Classification Rule Induction Framework

      • Frederic Stahl, Max Bramer, Mo Adda
      Pages 148-162

    7. Dynamic Score Combination: A Supervised and Unsupervised Score Combination Method

      • Roberto Tronci, Giorgio Giacinto, Fabio Roli
      Pages 163-177

    8. ODDboost: Incorporating Posterior Estimates into AdaBoost

      • Olga Barinova, Dmitry Vetrov
      Pages 178-190

  4. Ensemble Classifier Learning

    1. Ensemble Learning: A Study on Different Variants of the Dynamic Selection Approach

      • João Mendes-Moreira, Alipio Mario Jorge, Carlos Soares, Jorge Freire de Sousa
      Pages 191-205

    2. Relevance and Redundancy Analysis for Ensemble Classifiers

      • Rakkrit Duangsoithong, Terry Windeatt
      Pages 206-220

    3. Drift-Aware Ensemble Regression

      • Frank Rosenthal, Peter Benjamin Volk, Martin Hahmann, Dirk Habich, Wolfgang Lehner
      Pages 221-235

    4. Concept Drifting Detection on Noisy Streaming Data in Random Ensemble Decision Trees

      • Peipei Li, Xuegang Hu, Qianhui Liang, Yunjun Gao
      Pages 236-250

  5. Association Rules and Pattern Mining

    1. Mining Multiple Level Non-redundant Association Rules through Two-Fold Pruning of Redundancies

      • Corrado Loglisci, Donato Malerba
      Pages 251-265
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  1. Machine Learning and Data Mining in Pattern Recognition

Editors and Affiliations

  • Institut für Bildverarbeitung und angewandte Informatik, Leipzig, Germany

    Petra Perner

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