Advertisement

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)

Abstract

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.

Keywords

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

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Kwon, O., Yoo, K., Suh, E.: UbiDSS: A Proactive Intelligent Decision Support System as an Expert System Deploying Ubiquitous Computing Technologies. Expert Systems with Applications 28, 149–161 (2005)CrossRefGoogle Scholar
  2. 2.
    Dey, A.K.: Understanding and Using Context. Personal and Ubiquitous Computing 5, 4–7 (2001)CrossRefGoogle Scholar
  3. 3.
    Patterson, D., Liao, L., Fox, D., Kautz, H.: Inferring High-Level Behavior from Low-Level Sensors. In: Dey, A.K., Schmidt, A., McCarthy, J.F. (eds.) UbiComp 2003. LNCS, vol. 2864, pp. 73–89. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  4. 4.
    Hwang, K.S., Cho, S.B.: Landmark Detection from Mobile Life Log Using a Modular Bayesian Network Model. Expert Systems with Applications 36, 12065–12076 (2009)CrossRefGoogle Scholar
  5. 5.
    van Kasteren, T., Kröse, B.: Bayesian Activity Recognition in Residence for Elders. Intelligent Environments, 209–212 (2007)Google Scholar
  6. 6.
    Sánchez, D., Tentori, M., Favela, J.: Activity Recognition for the Smart Hospital. IEEE Intelligent Systems 23, 50–57 (2008)CrossRefGoogle Scholar
  7. 7.
    Byun, H.E., Cheverst, K.: Utilizing Context History to Provide Dynamic Adaptations. Applied Artificial Intelligence 18, 533–548 (2004)CrossRefGoogle Scholar
  8. 8.
    Lum, W.Y., Lau, F.C.M.: A Context-Aware Decision Engine for Content Adaptation. IEEE Pervasive Computing 1, 41–49 (2002)CrossRefGoogle Scholar
  9. 9.
    Matsuo, Y., Okazaki, N., Izumi, K., Nakamura, Y., Nishimura, T., Hasida, K., Nakashima, H.: Inferring Long-Term User Properties Based on Users’ Location History. In: 20th International Joint Conference on Artificial Intelligence (IJCAI), pp. 2159–2165 (2007)Google Scholar
  10. 10.
    Pearl, J.: Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann, San Mateo (1988)zbMATHGoogle Scholar
  11. 11.
    Bishop, C.M.: Pattern Recognition and Machine Learning. Springer, New York (2006)zbMATHGoogle Scholar
  12. 12.
    Witten, I.H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann, San Francisco (2005)zbMATHGoogle Scholar
  13. 13.
    Kittler, J.: Combining Classifiers: A Theoretical Framework. Pattern Analysis & Applications 1, 18–27 (1998)CrossRefGoogle Scholar
  14. 14.
    Polikar, R.: Ensemble Based Systems in Decision Making. IEEE Circuits and Systems Magazine 6, 21–45 (2006)CrossRefGoogle Scholar
  15. 15.
    Breiman, L.: Bagging Predictors. Machine Learning 24, 123–140 (1996)zbMATHGoogle Scholar
  16. 16.
    Schapire, R.E.: The Strength of Weak Learnability. Machine Learning 5, 197–227 (1990)Google Scholar
  17. 17.
    Ho, T.K.: The Random Subspace Method for Constructing Decision Forests. IEEE Transactions on Pattern Analysis and Machine Intelligence 20, 832–844 (1998)CrossRefGoogle Scholar
  18. 18.
    Kuncheva, L.I.: Combining Pattern Classifiers: Methods and Algorithms. Wiley-Interscience, New Jersey (2004)CrossRefzbMATHGoogle Scholar
  19. 19.
    Wolpert, D.H.: Stacked Generalization. Neural Networks 5, 241–259 (1992)CrossRefGoogle Scholar
  20. 20.
    Seewald, A., Fürnkranz, J.: An Evaluation of Grading Classifiers. In: Advances in Intelligent Data Analysis, pp. 115–124 (2001)Google Scholar
  21. 21.
    Hsieh, N.C., Hung, L.P.: A Data Driven Ensemble Classifier for Credit Scoring Analysis. Expert Systems with Applications 37, 534–545 (2010)CrossRefGoogle Scholar
  22. 22.
    Zhou, L., Lai, K.K., Yu, L.: Least Squares Support Vector Machines Ensemble Models for Credit Scoring. Expert Systems with Applications 37, 127–133 (2010)CrossRefGoogle Scholar
  23. 23.
    Twala, B.: Multiple Classifier Application to Credit Risk Assessment. Expert Systems with Applications 37, 3326–3336 (2010)CrossRefGoogle Scholar
  24. 24.
    Yu, L., Yue, W., Wang, S., Lai, K.K.: Support Vector Machine Based Multiagent Ensemble Learning for Credit Risk Evaluation. Expert Systems with Applications 37, 1351–1360 (2010)CrossRefGoogle Scholar
  25. 25.
    Hung, C., Chen, J.H.: A Selective Ensemble Based on Expected Probabilities for Bankruptcy Prediction. Expert Systems with Applications 36, 5297–5303 (2009)CrossRefGoogle Scholar
  26. 26.
    Das, R., Sengur, A.: Evaluation of Ensemble Methods for Diagnosing of Valvular Heart Disease. Expert Systems with Applications 37, 5110–5115 (2010)CrossRefGoogle Scholar
  27. 27.
    Eom, J.H., Kim, S.C., Zhang, B.T.: AptaCDSS-E: A Classifier Ensemble-Based Clinical Decision Support System for Cardiovascular Disease Level Prediction. Expert Systems with Applications 34, 2465–2479 (2008)CrossRefGoogle Scholar
  28. 28.
    Chen, S., Wang, W., Van Zuylen, H.: Construct Support Vector Machine Ensemble to Detect Traffic Incident. Expert Systems with Applications 36, 10976–10986 (2009)CrossRefGoogle Scholar
  29. 29.
    Anagnostopoulos, T., Anagnostopoulos, C., Hadjiefthymiades, S., Kyriakakos, M., Kalousis, A.: Predicting the Location of Mobile Users: A Machine Learning Approach. In: Int’l Conf. Pervasive Services, pp. 65–72 (2009)Google Scholar
  30. 30.
    Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA Data Mining Software: An Update. ACM SIGKDD Explorations Newsletter 11, 10–18 (2009)CrossRefGoogle Scholar
  31. 31.
    Quinlan, J.R.: Induction of Decision Trees. Machine Learning 1, 81–106 (1986)Google Scholar
  32. 32.
    Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann, San Mateo (1993)Google Scholar
  33. 33.
    Breiman, L.: Classification and Regression Trees. Chapman & Hall/CRC (1984)Google Scholar
  34. 34.
    Cooper, G.F., Herskovits, E.: A Bayesian Method for the Induction of Probabilistic Networks from Data. Machine Learning 9, 309–347 (1992)zbMATHGoogle Scholar
  35. 35.
    Platt, J.C.: Fast Training of Support Vector Machines Using Sequential Minimal Optimization. In: Advances in Kernel Methods: Support Vector Learning, pp. 185–208. MIT Press, Cambridge (1999)Google Scholar
  36. 36.
    Aha, D.W., Kibler, D., Albert, M.K.: Instance-Based Learning Algorithms. Machine Learning 6, 37–66 (1991)Google Scholar

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

Personalised recommendations