Pattern Analysis & Applications

, Volume 6, Issue 3, pp 245–256 | Cite as

New Applications of Ensembles of Classifiers

Original Paper

Abstract

Combination (ensembles) of classifiers is now a well established research line. It has been observed that the predictive accuracy of a combination of independent classifiers excels that of the single best classifier. While ensembles of classifiers have been mostly employed to achieve higher recognition accuracy, this paper focuses on the use of combinations of individual classifiers for handling several problems from the practice in the machine learning, pattern recognition and data mining domains. In particular, the study presented concentrates on managing the imbalanced training sample problem, scaling up some preprocessing algorithms and filtering the training set. Here, all these situations are examined mainly in connection with the nearest neighbour classifier. Experimental results show the potential of multiple classifier systems when applied to those situations.

Algorithm scalability Ensembles; Filtering Outliers Imbalanced training sample Nearest neighbour rule Preprocessing techniques 

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Copyright information

© Springer-Verlag London Limited 2003

Authors and Affiliations

  1. 1.Institute Tecnológico de TolucaMetepecMéxico.
  2. 2.Universitat Jaume ICastellóSpain

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