Discovering Relevant Sensor Data by Q-Analysis

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


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.


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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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

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

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