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Visualizing High Dimensional Classifier Performance Data

  • Rocio Alaiz-Rodríguez
  • Nathalie Japkowicz
  • Peter Tischer
Part of the Studies in Computational Intelligence book series (SCI, volume 223)

Abstract

Classifier performance evaluation, which typically yields a vast number of results, may be approached as a problem of analyzing high dimensional data. Conducting an exploratory analysis of visual representations of this evaluation data enables us to exploit the advantages of the powerful human visual capabilities. This allows us to gain insight into the performance data, interact with it and draw meaningful conclusions about the classifiers and domains under study. We illustrate how visual techniques, based on a projection from a high dimensional space to a lower dimensional one, enable such an exploratory process. Moreover, this approach can be viewed as a generalization of conventional evaluation procedures based on point metrics that necessarily imply a higher loss of information. Finally, we show that within this framework, the user is able to study the evaluation data from a classifier point of view and from a domain point of view, which is infeasible with traditional evaluation methods.

Keywords

Root Mean Square Error Performance Data Projection Method High Dimensional Space Original Space 
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 2009

Authors and Affiliations

  • Rocio Alaiz-Rodríguez
    • 1
  • Nathalie Japkowicz
    • 2
  • Peter Tischer
    • 3
  1. 1.Dpto. de Ingeniería Eléctrica y de Sistemas y AutomaticaUniversidad de LeónLeónSpain
  2. 2.School of Information Technology and EngineeringUniversity of OttawaStn. A OttawaCanada
  3. 3.Clayton School of Information TechnologyMonash UniversityAustralia

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