8. MCS 2009: Reykjavik, Iceland

ECOC, Boosting and Bagging

• Raymond S. Smith, Terry Windeatt:
  The Bias Variance Trade-Off in Bootstrapped Error Correcting Output Code Ensembles. 1-10
  
• Sergio Escalera, Oriol Pujol, Petia Radeva:
  Recoding Error-Correcting Output Codes. 11-21
  
• Satoshi Shirai, Mineichi Kudo, Atsuyoshi Nakamura:
  Comparison of Bagging and Boosting Algorithms on Sample and Feature Weighting. 22-31
  
• Goo Jun, Joydeep Ghosh:
  Multi-class Boosting with Class Hierarchies. 32-41
  
• Goo Jun, Joydeep Ghosh:
  Hybrid Hierarchical Classifiers for Hyperspectral Data Analysis. 42-51
  

MCS in Remote Sensing

• Peijun Du, Wei Zhang, Hao Sun:
  Multiple Classifier Combination for Hyperspectral Remote Sensing Image Classification. 52-61
  
• Xavier Ceamanos, Björn Waske, Jon Atli Benediktsson, Jocelyn Chanussot, Johannes R. Sveinsson:
  Ensemble Strategies for Classifying Hyperspectral Remote Sensing Data. 62-71
  

Unbalanced Data and Decision Templates

• David M. J. Tax, Marco Loog, Robert P. W. Duin:
  Optimal Mean-Precision Classifier. 72-81
  
• Muhammad Atif Tahir, Josef Kittler, Krystian Mikolajczyk, Fei Yan:
  A Multiple Expert Approach to the Class Imbalance Problem Using Inverse Random under Sampling. 82-91
  
• Mohamed Farouk Abdel Hady, Friedhelm Schwenker:
  Decision Templates Based RBF Network for Tree-Structured Multiple Classifier Fusion. 92-101
  

Stacked Generalization and Active Learning

• Narayanan U. Edakunni, Sethu Vijayakumar:
  Efficient Online Classification Using an Ensemble of Bayesian Linear Logistic Regressors. 102-111
  
• Samuel Robert Reid, Gregory Z. Grudic:
  Regularized Linear Models in Stacked Generalization. 112-121
  
• Davy Sannen, Hendrik Van Brussel:
  Active Grading Ensembles for Learning Visual Quality Control from Multiple Humans. 122-131
  
• Battista Biggio, Giorgio Fumera, Fabio Roli:
  Multiple Classifier Systems for Adversarial Classification Tasks. 132-141
  

Concept Drift, Missing Values and Random Forest

• Ryan Elwell, Robi Polikar:
  Incremental Learning of Variable Rate Concept Drift. 142-151
  
• Luca Didaci, Gian Luca Marcialis, Fabio Roli:
  Semi-supervised Co-update of Multiple Matchers. 152-160
  
• David Windridge, Norman Poh, Vadim Mottl, Alexander Tatarchuk, Andrey Eliseyev:
  Handling Multimodal Information Fusion with Missing Observations Using the Neutral Point Substitution Method. 161-170
  
• Simon Bernard, Laurent Heutte, Sébastien Adam:
  Influence of Hyperparameters on Random Forest Accuracy. 171-180
  

SVM Ensembles

• Albert D. Shieh, David F. Kamm:
  Ensembles of One Class Support Vector Machines. 181-190
  
• Jesús Maudes, Juan José Rodríguez, Cesar García-Osorio:
  Disturbing Neighbors Ensembles for Linear SVM. 191-200
  

Fusion of Graphs, Concepts and Categorical Data

• Wan-Jui Lee, Robert P. W. Duin:
  A Labelled Graph Based Multiple Classifier System. 201-210
  
• Kaspar Riesen, Horst Bunke:
  Cluster Ensembles Based on Vector Space Embeddings of Graphs. 211-221
  
• Amir Ahmad, Gavin Brown:
  Random Ordinality Ensembles A Novel Ensemble Method for Multi-valued Categorical Data. 222-231
  
• Giorgio Valentini:
  True Path Rule Hierarchical Ensembles. 232-241
  

Clustering

• Manuela Zanda, Gavin Brown:
  A Study of Semi-supervised Generative Ensembles. 242-251
  
• Mingmin Chi, Youdong Miao, Youze Tang, Jon Atli Benediktsson, Xuanjing Huang:
  Hierarchical Ensemble Support Cluster Machine. 252-261
  
• Oriol Pujol, Eloi Puertas, Carlo Gatta:
  Multi-scale Stacked Sequential Learning. 262-271
  
• Michal Haindl, Stanislav Mikes, Pavel Pudil:
  Unsupervised Hierarchical Weighted Multi-segmenter. 272-282
  
• Yuhua Gu, Lawrence O. Hall, Dmitry B. Goldgof:
  Ant Clustering Using Ensembles of Partitions. 283-292
  

Classifier and Feature Selection

• Nan Li, Zhi-Hua Zhou:
  Selective Ensemble under Regularization Framework. 293-303
  
• Petr Somol, Jiri Grim, Pavel Pudil:
  Criteria Ensembles in Feature Selection. 304-313
  
• Francesco Gargiulo, Ludmila I. Kuncheva, Carlo Sansone:
  Network Protocol Verification by a Classifier Selection Ensemble. 314-323
  
• Alexander Tatarchuk, Valentina Sulimova, David Windridge, Vadim Mottl, Mikhail Lange:
  Supervised Selective Combining Pattern Recognition Modalities and Its Application to Signature Verification by Fusing On-Line and Off-Line Kernels. 324-334
  

Theory of MCS

• Matthew Prior, Terry Windeatt:
  Improved Uniformity Enforcement in Stochastic Discrimination. 335-343
  
• Gavin Brown:
  An Information Theoretic Perspective on Multiple Classifier Systems. 344-353
  
• Amber Tomas:
  Constraints in Weighted Averaging. 354-363
  
• Kai Ming Ting, Jonathan R. Wells, Swee Chuan Tan, Shyh Wei Teng, Geoffrey I. Webb:
  FaSS: Ensembles for Stable Learners. 364-374
  

MCS Methods and Applications

• Björn Waske, Jon Atli Benediktsson, Johannes R. Sveinsson:
  Classifying Remote Sensing Data with Support Vector Machines and Imbalanced Training Data. 375-384
  
• Michael J. Procopio, W. Philip Kegelmeyer, Gregory Z. Grudic, Jane Mulligan:
  Terrain Segmentation with On-Line Mixtures of Experts for Autonomous Robot Navigation. 385-397
  
• Peijun Du, Guangli Li, Wei Zhang, Xiaomei Wang, Hao Sun:
  Consistency Measure of Multiple Classifiers for Land Cover Classification by Remote Sensing Image. 398-407
  
• Peijun Du, Hao Sun, Wei Zhang:
  Target Identification from High Resolution Remote Sensing Image by Combining Multiple Classifiers. 408-417
  
• Sergey Tulyakov, Venu Govindaraju:
  Neural Network Optimization for Combinations in Identification Systems. 418-427
  
• Waleed M. Azmy, Neamat El Gayar, Amir F. Atiya, Hisham El-Shishiny:
  MLP, Gaussian Processes and Negative Correlation Learning for Time Series Prediction. 428-437
  
• Ingrid Visentini, Josef Kittler, Gian Luca Foresti:
  Diversity-Based Classifier Selection for Adaptive Object Tracking. 438-447
  
• Matteo Re, Giorgio Valentini:
  Ensemble Based Data Fusion for Gene Function Prediction. 448-457
  
• Jian-Wu Xu, Vartika Singh, Venu Govindaraju, Depankar Neogi:
  A Cascade Multiple Classifier System for Document Categorization. 458-467
  
• Marco Loog, Yan Li, David M. J. Tax:
  Maximum Membership Scale Selection. 468-477
  
• Chun-Xia Zhang, Robert P. W. Duin:
  An Empirical Study of a Linear Regression Combiner on Multi-class Data Sets. 478-487
  
• Amir Ahmad, Gavin Brown:
  A Study of Random Linear Oracle Ensembles. 488-497
  
• Kai Lienemann, Thomas Plötz, Gernot A. Fink:
  Stacking for Ensembles of Local Experts in Metabonomic Applications. 498-508
  
• Kai Ming Ting, Lian Zhu:
  Boosting Support Vector Machines Successfully. 509-518
  

Invited Papers

• Melba Crawford, Wonkook Kim:
  Manifold Learning for Multi-classifier Systems via Ensembles. 519-528
  
• Zhi-Hua Zhou:
  When Semi-supervised Learning Meets Ensemble Learning. 529-538