Continuous time Bayesian network classifiers

Journal of Biomedical Informatics

Volumn 45, Issue 6, 2012, Pages 1108-1119

  Stella, F ^a   Amer, Y ^a  

^a UNIVERSITY OF MILANO BICOCCA   (Italy)

Author keywords

Bayesian classifiers; Continuous time; Multivariate trajectory

Indexed keywords

BAYESIAN CLASSIFIER; BAYESIAN NETWORK CLASSIFIERS; CLASSIFICATION ACCURACY; CONTINUOUS TIME; DISCRETE ATTRIBUTES; EXACT INFERENCE; MOTOR REHABILITATION; NAIVE BAYES CLASSIFIERS; NEUROLOGICAL PATIENT; REAL-TIME FEEDBACK; SUPERVISED CLASSIFICATION; TREE AUGMENTED NAIVE BAYES CLASSIFIERS;

CLASSIFIERS; CONTINUOUS TIME SYSTEMS; INFERENCE ENGINES; LEARNING ALGORITHMS; LEARNING SYSTEMS; PATIENT REHABILITATION; TRAJECTORIES;

BAYESIAN NETWORKS;

ACCURACY; ALGORITHM; ARTICLE; BAYESIAN LEARNING; FALSE POSITIVE RESULT; PREDICTION; PRIORITY JOURNAL;

ALGORITHMS; BAYES THEOREM; HUMANS; PATTERN RECOGNITION, AUTOMATED;

EID: 84869883404     PISSN: 15320464     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jbi.2012.07.002     Document Type: Article

References (33)

1. Rabiner L.R. A tutorial on hidden Markov models and selected applications in speech recognition. Proc IEEE 1989, 77(2):257-286.
2. Dean T., Kanazawa K. A model for reasoning about persistence and causation. Comput Intell 1989, 5(2):142-150.
3. Dagum P, Galper A, Horvitz E. Dynamic network models for forecasting. In: Uncertainty in artificial intelligence; 1992. p. 41-8.
4. Dagum P, Galper A. Forecasting sleep apnea with dynamic network models. In: Uncertainty in artificial intelligence; 1993. p. 64-71.
5. Pavlovic V., Frey B.J., Huang T.S. Time-series classification using mixed-state dynamic Bayesian networks. Comput Vis Pattern Recognit 1999, 2:2609.
6. Obermaier B., Guger C., Neuper C., Pfurtscheller G. Hidden Markov models for online classification of single trial eeg data. Pattern Recognit Lett 2001, 22(12):1299-1309.
7. Andersson M. Classification of aerial missions using hidden Markov models. Lecture notes in computer science 2003, 125-136. Springer, Berlin/Heidelberg. T. Nielsen, N. Zhang (Eds.).
8. Gu H., Ji Q. Facial event classification with task oriented dynamic Bayesian network. Comput Vis Pattern Recognit 2004, 2:870-875.
9. Riggelsen C., Ohrnberger M., Scherbaum F. Dynamic Bayesian networks for real-time classification of seismic signals. Lecture notes in computer science 2007, vol. 4702:565-572. Springer, Berlin/Heidelberg. J. Kok, J. Koronacki, R. Lopez de Mantaras, S. Matwin, D. Mladenic, A. Skowron (Eds.).
10. Marti{dotless}́nez M, Sucar LE. Learning dynamic naive Bayesian classifiers. In: FLAIRS conference; 2008. p. 655-9.
11. Friedman N., Geiger D., Goldszmidt M. Bayesian network classifiers. Mach Learn 1997, 29:131-163.
12. Tucker A., Hoen P.A.C., Vinciotti V., Liu X. Temporal Bayesian classifiers for modelling muscular dystrophy expression data. Intell Data Anal 2006, 10(5):441-455.
13. Tucker A., Vinciotti V., Liu X., Garway-Heath D. A spatio-temporal Bayesian network classifier for understanding visual field deterioration. Artif Intell Med 2005, 34(2):163-177.
14. Fisher M., Gabbay D., Vila L. Handbook of temporal reasoning in artificial intelligence (foundations of artificial intelligence) 2005, Elsevier Science Inc., New York, NY, USA.
15. Combi C., Keravnou-Papailiou E., Shahar Y. Temporal information systems in medicine 2010, Springer Publishing Company, Incorporated. 1st ed.
16. Pearl J. Probabilistic reasoning in intelligent systems: networks of plausible inference 1988, Morgan Kaufmann.
17. Pearl J. Causality: models, reasoning and inference 2000, Cambridge University Press.
18. Nodelman U, Shelton C, Koller D. Continuous time Bayesian networks. In: Uncertainty in artificial intelligence; 2002. p. 78-387.
19. Tormene P., Giorgino T., Quaglini S., Stefanelli M. Matching incomplete time series with dynamic time warping: an algorithm and an application to post-stroke rehabilitation. Artif Intell Med 2009, 45(1):11-34.
20. Jensen F.V., Nielsen T.D. Bayesian networks and decision graphs 2007, Springer-Verlag. 2nd ed.
21. Nodelman U, Horvitz E. Continuous time Bayesian networks for inferring users presence and activities with extensions for modeling and evaluation. Tech. Rep.; 2003.
22. Boudali H., Dugan J.B. A continuous-time Bayesian network reliability modeling, and analysis framework. IEEE Trans Reliab 2006, 55(1):86-97.
23. Xu J, Shelton CR. Continuous time Bayesian networks for host level network intrusion detection. In: ECML/PKDD (2); 2008. p. 613-27.
24. Fan Y, Shelton CR. Learning continuous-time social network dynamics. In: Uncertainty in artificial intelligence; 2009. p. 161-8.
25. Gatti E., Luciani D., Stella F. A continuous time Bayesian network model for cardiogenic heart failure. Flex Serv Manufact J 2011, 1-20.
26. Nodelman U, Koller D, Shelton CR. Expectation propagation for continuous time Bayesian networks. In: Uncertainty in artificial intelligence; 2005a. p. 431-40.
27. Saria S., Nodelman U., Koller D. Reasoning at the right time granularity 2007, 1-9.
28. Fan Y., Xu J., Shelton C.R. Importance sampling for continuous time Bayesian networks. J Mach Learn Res 2010, 11:2115-2140.
29. El-Hay T, Friedman N, Kupferman R. Gibbs sampling in factorized continuous-time Markov processes. In: Uncertainty in artificial intelligence; 2008. p. 169-78.
30. Nodelman U, Shelton C, Koller D. Expectation maximization and complex duration distributions for continuous time Bayesian networks. In: Uncertainty in artificial intelligence; 2005b. p. 421-30.
31. Witten I.H., Frank E. Data mining: practical machine learning tools and techniques with Java implementations 1999, Morgan Kaufmann, ISBN: 1-55860-552-5.
32. Bellazzi R., Zupan B. Predictive data mining in clinical medicine: current issues and guidelines. I J Med Inform 2008, 77(2):81-97.
33. Demsar J. Statistical comparisons of classifiers over multiple data sets. J Mach Learn Res 2006, 7:1-30.