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.

Invited Papers.- Classifier Ensembles for Changing Environments.- A Generic Sensor Fusion Problem: Classification and Function Estimation.- Bagging and Boosting.- AveBoost2: Boosting for Noisy Data.- Bagging Decision Multi-trees.- Learn++.MT: A New Approach to Incremental Learning.- Beyond Boosting: Recursive ECOC Learning Machines.- Exact Bagging with k-Nearest Neighbour Classifiers.- Combination Methods.- Yet Another Method for Combining Classifiers Outputs: A Maximum Entropy Approach.- Combining One-Class Classifiers to Classify Missing Data.- Combining Kernel Information for Support Vector Classification.- Combining Classifiers Using Dependency-Based Product Approximation with Bayes Error Rate.- Combining Dissimilarity-Based One-Class Classifiers.- A Modular System for the Classification of Time Series Data.- A Probabilistic Model Using Information Theoretic Measures for Cluster Ensembles.- Classifier Fusion Using Triangular Norms.- Dynamic Integration of Regression Models.- Dynamic Classifier Selection by Adaptive k-Nearest-Neighbourhood Rule.- Design Methods.- Spectral Measure for Multi-class Problems.- The Relationship between Classifier Factorisation and Performance in Stochastic Vector Quantisation.- A Method for Designing Cost-Sensitive ECOC.- Building Graph-Based Classifier Ensembles by Random Node Selection.- A Comparison of Ensemble Creation Techniques.- Multiple Classifiers System for Reducing Influences of Atypical Observations.- Sharing Training Patterns among Multiple Classifiers.- Performance Analysis.- First Experiments on Ensembles of Radial Basis Functions.- Random Aggregated and Bagged Ensembles of SVMs: An Empirical Bias–Variance Analysis.- Building Diverse Classifier Outputs to Evaluate the Behavior of Combination Methods: The Case of TwoClassifiers.- An Empirical Comparison of Hierarchical vs. Two-Level Approaches to Multiclass Problems.- Experiments on Ensembles with Missing and Noisy Data.- Applications.- Induced Decision Fusion in Automated Sign Language Interpretation: Using ICA to Isolate the Underlying Components of Sign.- Ensembles of Classifiers Derived from Multiple Prototypes and Their Application to Handwriting Recognition.- Network Intrusion Detection by a Multi-stage Classification System.- Application of Breiman’s Random Forest to Modeling Structure-Activity Relationships of Pharmaceutical Molecules.- Experimental Study on Multiple LDA Classifier Combination for High Dimensional Data Classification.- Physics-Based Decorrelation of Image Data for Decision Level Fusion in Face Verification.- High Security Fingerprint Verification by Perceptron-Based Fusion of Multiple Matchers.- Second Guessing a Commercial’Black Box’ Classifier by an’In House’ Classifier: Serial Classifier Combination in a Speech Recognition Application.