Evolving ensemble of classifiers in random subspace

/in , , /by
Evolving ensemble of classifiers in random subspace

Ko, Albert Hung Ren and Sabourin, Robert and De Souza Britto, Alceu

GECCO 2006 – Genetic and Evolutionary Computation Conference 2006

Abstract : 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. Copyright 2006 ACM.

You might also like
Pattern spotting and image retrieval in historical documents using deep hashing
Familiarity discrimination of radar pulses
Overfitting cautious selection of classifier ensembles with genetic algorithms
Cross-domain face synthesis using a controllable GAN
Kernel clustering: Density biases and solutions
Graph cuts with invariant object-interaction priors: application to intervertebral disc segmentation
Automatic genre classification of music content
Detecting left ventricular impaired relaxation in cardiac MRI using moving mesh correspondences