Orthogonal Decision Trees for Resource-Constrained Physiological Data Stream Monitoring Using Mobile Devices

  • Haimonti Dutta
  • Hillol Kargupta
  • Anupam Joshi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3769)


This paper considers the problem of monitoring physiological data streams obtained from resource-constrained wearable sensing devices for pervasive health-care management. It considers Orthogonal decision trees (ODTs) that offer an effective way to construct a redundancy-free, accurate, and meaningful representation of large decision-tree-ensembles often created by popular techniques such as Bagging, Boosting, Random Forests and many distributed and data stream mining algorithms. ODTs are functionally orthogonal to each other and they correspond to the principal components of the underlying function space. This paper offers experimental results to document the performance of ODTs on grounds of accuracy, model complexity, and resource consumption.


Decision Tree Random Forest Data Stream West Nile Virus Fourier Spectrum 
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 2005

Authors and Affiliations

  • Haimonti Dutta
    • 1
  • Hillol Kargupta
    • 1
  • Anupam Joshi
    • 1
  1. 1.Department of Computer Science and Electrical EngineeringUniversity of Maryland Baltimore CountyBaltimoreUSA

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