A General Bayesian Network-Assisted Ensemble System for Context Prediction: An Emphasis on Location Prediction

  • Kun Chang Lee
  • Heeryon Cho
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6485)


Context prediction, highlighted by accurate location prediction, has been at the heart of ubiquitous decision support systems. To improve the prediction accuracy of such systems, various methods have been proposed and tested; these include Bayesian networks, decision classifiers, and SVMs. Still, greater accuracy may be achieved when individual classifiers are integrated into an ensemble system. Meanwhile, General Bayesian Network (GBN) classifier possesses a great potential as an accurate decision support engine for context prediction. To leverage the power of both the GBN and the ensemble system, we propose a GBN-assisted ensemble system for location prediction. The proposed ensemble system uses variables extracted from Markov blanket of the GBN’s class node to integrate GBN, decision tree, and SVM. The proposed system was applied to a real-world location prediction dataset, and promising results were obtained. Practical implications are discussed.


Context Prediction Location Prediction Ensemble Methods General Bayesian Network GBN-Assisted Ensemble Classifier ID3 C4.5 CART SVM 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Kun Chang Lee
    • 1
    • 2
  • Heeryon Cho
    • 2
  1. 1.SKK Business SchoolSungkyunkwan UniversitySeoulRepublic of Korea
  2. 2.Department of Interaction ScienceSungkyunkwan UniversitySeoulRepublic of Korea

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