Evolving ensemble of classifiers in random subspace

Various methods for ensemble selection and classifier combination have been designed to optimize the results of ensembles of classifiers. Genetic algorithm (GA) which uses the diversity for the ensemble selection could be very time consuming. We propose compound diversity functions as objective functions for a faster and more effective GA searching. Classifiers selected by GA are combined by a proposed pairwise confusion matrix transformation, which offer strong performance boost for EoCs. Categories and Subject Descriptors I.5.2 [Pattern Recognition]: Design Methodology--classifier design and evaluation General Terms Pairwise Confusion Matrix Transformation Algorithm Keywords Fusion Function, Combining Classifiers, Diversity, Confusion Matrix, Pattern Recognition, Majority Voting, Ensemble of Learning Machines.

• Type: Conference
• Year: 2006
• Where: GECCO
• Authors: Albert Hung-Ren Ko, Robert Sabourin, Alceu de Souza Britto Jr.
• Keywords: Confusion Matrix Transformation, Ensemble Selection, GECCO 2006, Optimization, Pairwise Confusion Matrix