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• [P1288] A Novel Dynamic Fusion Method Using Localized Generalization Error Model
  Author(s): Daniel S. Yeung, Patrick P. K. Chan
  
  

  Abstract
  A new dynamic classifier fusion method named LGEM Fusion Method (LFM) for Multiple Classifier Systems (MCSs) is proposed. The localized generalization error upper bound for the neighborhood of a testing sample is calculated and used to estimate the local competence of base classifiers in MCSs. Different from the recent dynamic classifier selection methods, the proposed method considers not only the training error but also the sensitivity of the base classifier. Experimental results show that the MCSs using LFM is more accurate than other popular dynamic fusion methods.

  
   

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