Three Data Partitioning Strategies for Building Local Classifiers

Part of the Studies in Computational Intelligence book series (SCI, volume 373)


Divide-and-conquer approach has been recognized in multiple classifier systems aiming to utilize local expertise of individual classifiers. In this study we experimentally investigate three strategies for building local classifiers that are based on different routines of sampling data for training. The first two strategies are based on clustering the training data and building an individual classifier for each cluster or a combination. The third strategy divides the training set based on a selected feature and trains a separate classifier for each subset. Experiments are carried out on simulated and real datasets. We report improvement in the final classification accuracy as a result of combining the three strategies.


Real Dataset Class Membership Testing Error Random Oracle Research Proposal 
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© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  1. 1.Eindhoven University of TechnologyEindhoventhe Netherlands

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