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Discovering Relevant Sensor Data by Q-Analysis

  • Pejman Iravani
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4020)

Abstract

This paper proposes a novel method for supervised classification based on the methodology of Q-analysis. The classification is based on finding ‘relevant’ structures in the features describing the data, and using them to define each of the classes. The features not included in the structural definition of a class are considered as ‘irrelevant’. The paper uses three different data-sets to experimentally validate the method.

Keywords

Heuristic Method Feature Selection Method Target Class Binary Feature Sepal Length 
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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References

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    Johnson, J.H.: Stars, Maximal Rectangles, Lattices: a new perspective on Q-analysis. International Journal of Man-Machine Studies 24, 293–299 (1986)CrossRefGoogle Scholar
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    Iravani, P., Johnson, J.H., Rapanotti, L.: Robotics and the Q-analysis of behaviour: International Symposium on Artificial Life and Robotics (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Pejman Iravani
    • 1
  1. 1.The Open UniversityMilton KeynesUK

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