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Improving the Competency of Classifiers through Data Generation

  • Herna L. Viktor
  • Iryna Skrypnik
Conference paper

Abstract

This paper describes a hybrid approach in which sub-symbolic neural networks and symbolic machine learning algorithms are grouped into an ensemble of classifiers. Initially each classifier determines which portion of the data it is most competent in. The competency information is used to generated new data that are used for further training and prediction. The application of this approach in a difficult to learn domain shows an increase in the predictive power, in terms of the accuracy and level of competency of both the ensemble and the component classifiers.

Keywords

Problem Domain Training Instance Data Generation Process Disjunctive Normal Form Decision Tree Algorithm 
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 Wien 2001

Authors and Affiliations

  • Herna L. Viktor
    • 1
  • Iryna Skrypnik
    • 2
  1. 1.Department of Informatics, School of Information TechnologyUniversity of PretoriaSouth Africa
  2. 2.Department of Computer Science and Information SystemUniversity of JyväskyläFinland

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