Analysis of Diversity Assurance Methods for Combined Classifiers

Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 184)

Summary

Assuring diversity of classifiers in an ensemble plays a crucial role in the multiple classifier system design. The paper presents a comparative study of selected methods which can assure the diversity by manipulating the individual classifier inputs i.e., they train learner using subspaces of a feature set or they try to exploit local competencies of individual classifier for a given subset of feature space. This work is a starting point for developing new methods of diversity assurance embedded in a multiple classifier system design. All methods had been evaluated on the basis of computer experiments which were carried out on benchmark datasets. On the basis of received results conclusions about the usefulness of examined methods for certain types of problems were drawn.

Keywords

Feature Space Benchmark Dataset Ensemble Method Feature Selection Algorithm Random Subspace 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Department of Systems and Computer NetworksWroclaw University of TechnologyWroclawPoland

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