An Efficient Strategy to Handle Complex Datasets Having Multimodal Distribution

  • Samira GhodratnamaEmail author
  • Reza Boostani
Part of the Emergence, Complexity and Computation book series (ECC, volume 14)


One of the main shortcomings of the conventional classifiers is appeared when facing with datasets having multimodal distribution. To overcome this drawback, here, an efficient strategy is proposed in which a clustering phase is firstly executed over all class samples to partition the feature space into separate subspaces (clusters). Since in clustering label of samples are not considered, each cluster contains impure samples belonging to different classes. The next phase is to apply a classifier to each of the created clusters. The main advantage of this proposed distributed approach is to simplify a complex pattern recognition problem by training a specific classifier for each subspace. It is expected applying an efficient classifier to a local cluster leads to better results compared to apply it to several scattered clusters. In the validation and test phases, before make a decision about which classifier should be applied, we should find the nearest cluster to the input sample and then utilize the corresponding trained classifier. Experimental results over different UCI datasets demonstrate a significant supremacy of the proposed distributed classifier system in comparison with single classifier approaches.


Distributed classifiers classifier ensembles subspace classification distributed learning complex systems 


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.School of Electrical and Computer EngineeringShiraz UniversityShirazIran

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