Multiple Classifier Systems

1. Front Matter

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2. ECOC, Boosting and Bagging

   1. The Bias Variance Trade-Off in Bootstrapped Error Correcting Output Code Ensembles

      • Raymond S. Smith, Terry Windeatt

      Pages 1-10

   2. Recoding Error-Correcting Output Codes

      • Sergio Escalera, Oriol Pujol, Petia Radeva

      Pages 11-21

   3. Comparison of Bagging and Boosting Algorithms on Sample and Feature Weighting

      • Satoshi Shirai, Mineichi Kudo, Atsuyoshi Nakamura

      Pages 22-31

   4. Multi-class Boosting with Class Hierarchies

      • Goo Jun, Joydeep Ghosh

      Pages 32-41

3. MCS in Remote Sensing

   1. Hybrid Hierarchical Classifiers for Hyperspectral Data Analysis

      • Goo Jun, Joydeep Ghosh

      Pages 42-51

   2. Multiple Classifier Combination for Hyperspectral Remote Sensing Image Classification

      • Peijun Du, Wei Zhang, Hao Sun

      Pages 52-61

   3. Ensemble Strategies for Classifying Hyperspectral Remote Sensing Data

      • Xavier Ceamanos, Björn Waske, Jón Atli Benediktsson, Jocelyn Chanussot, Johannes R. Sveinsson

      Pages 62-71

4. Unbalanced Data and Decision Templates

   1. Optimal Mean-Precision Classifier

      • David M. J. Tax, Marco Loog, Robert P. W. Duin

      Pages 72-81

   2. A Multiple Expert Approach to the Class Imbalance Problem Using Inverse Random under Sampling

      • Muhammad Atif Tahir, Josef Kittler, Krystian Mikolajczyk, Fei Yan

      Pages 82-91

   3. Decision Templates Based RBF Network for Tree-Structured Multiple Classifier Fusion

      • Mohamed Farouk Abdel Hady, Friedhelm Schwenker

      Pages 92-101

5. Stacked Generalization and Active Learning

   1. Efficient Online Classification Using an Ensemble of Bayesian Linear Logistic Regressors

      • Narayanan U. Edakunni, Sethu Vijayakumar

      Pages 102-111

   2. Regularized Linear Models in Stacked Generalization

      • Sam Reid, Greg Grudic

      Pages 112-121

   3. Active Grading Ensembles for Learning Visual Quality Control from Multiple Humans

      • Davy Sannen, Hendrik Van Brussel

      Pages 122-131

   4. Multiple Classifier Systems for Adversarial Classification Tasks

      • Battista Biggio, Giorgio Fumera, Fabio Roli

      Pages 132-141

6. Concept Drift, Missing Values and Random Forest

   1. Incremental Learning of Variable Rate Concept Drift

      • Ryan Elwell, Robi Polikar

      Pages 142-151

   2. Semi-supervised Co-update of Multiple Matchers

      • Luca Didaci, Gian Luca Marcialis, Fabio Roli

      Pages 152-160

   3. Handling Multimodal Information Fusion with Missing Observations Using the Neutral Point Substitution Method

      • David Windridge, Norman Poh, Vadim Mottl, Alexander Tatarchuk, Andrey Eliseyev

      Pages 161-170

   4. Influence of Hyperparameters on Random Forest Accuracy

      • Simon Bernard, Laurent Heutte, Sébastien Adam

      Pages 171-180

7. SVM Ensembles

   1. Ensembles of One Class Support Vector Machines

      • Albert D. Shieh, David F. Kamm

      Pages 181-190

These proceedings are a record of the Multiple Classi?er Systems Workshop, MCS 2009, held at the University of Iceland, Reykjavik, Iceland in June 2009. Being the eighth in a well-established series of meetings providing an inter- tional forum for the discussion of issues in multiple classi?er system design, the workshop achieved its objective of bringing together researchers from diverse communities (neural networks,pattern recognition,machine learning and stat- tics) concerned with this research topic. From more than 70 submissions, the Program Committee selected 54 papers to create an interesting scienti?c program. The special focus of MCS 2009 was on the application of multiple classi?er systems in remote sensing. This part- ular application uses multiple classi?ers for raw data fusion, feature level fusion and decision level fusion. In addition to the excellent regular submission in the technical program, outstanding contributions were made by invited speakers Melba Crawford from Purdue University and Zhi-Hua Zhou of Nanjing Univ- sity. Papers of these talks are included in these workshop proceedings. With the workshop’sapplicationfocusbeingonremotesensing,Prof.Crawford’sexpertise in the use of multiple classi?cation systems in this context made the discussions on this topic at MCS 2009 particularly fruitful.

• Support Vector Machine
• algorithmic learning
• algorithms
• bayesian networks
• biometric authentication
• classification
• classifier systems
• data fragmentation
• decision trees
• diversity
• document analysis
• ensemble learning
• ensemble prediction
• image analysis
• pattern recognition

• Faculty of Electrical and Computer Engineering, University of Iceland, Reykjavik, Iceland

  Jón Atli Benediktsson

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

  Josef Kittler

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

  Fabio Roli

Policies and ethics