Application of Bayesian belief networks for context extraction from wireless sensors data

International Conference on Advanced Communication Technology, ICACT

Volumn , Issue , 2012, Pages 410-415

  Mittal, Sangeeta ^a   Aggarwal, Alok ^a   Maskara, S L ^b  

^a JAYPEE INSTITUTE OF INFORMATION TECHNOLOGY   (India)

^b Soura Niloy Housing Complex ^*  (India)

Author keywords

Bayesian Belief Networks; Context Extraction; Sensor data classification; Wireless Sensor Networks

Indexed keywords

BAYESIAN APPROACHES; CONTEXTUAL INFORMATION; FEATURE CLASSIFICATION; LOW POWER SENSOR; MARKOV MODEL; OTHER APPLICATIONS; SENSOR DATA; SENSORS DATA; SIMPLE RULES; WIRELESS SENSOR;

BAYESIAN NETWORKS; COMMUNICATION; DECISION TREES; IMAGE MATCHING; INNOVATION; MARKOV PROCESSES; SENSORS; WIRELESS SENSOR NETWORKS;

DATA MINING;

EID: 84860578026     PISSN: 17389445     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (15)

1. IST Advisory Group, Scenarios for Ambient Intelligence in 2010, European Commission, 2001.
2. Friedman, N., Geiger, D., Goldszmidt, M. (1997). Bayesian network classifiers. Machine Learning, 29, pp. 131-163.
3. J. Cheng and R. Greiner, Learning Bayesian Belief Network Classifiers: Algorithms and System, Lecture Notes in Computer Science, (2056) pages 141.151, Springer Verlag, 2001.
4. GF Cooper, E Herskovits, "A Bayesian method for the induction of probabilistic networks from data", Mach Learning 9(4):309-347(1992)
5. Intelberkely research lab [Online] http://db.csail.mit.edu/labdata/ labdata.html
6. Leray P, Francois O : BNT structure learning package: documentation and experiments. Technical Report 2004.
7. O.C.H. François and P. Leray. Learning the tree augmented naive bayes classifier from incomplete datasets. In Proceedings of the Third European Workshop on Probabilistic Graphical Models (PGM'06), pages 91-98, Prague, Czech Republic, Sep 2006.
8. J. Heckerman, D., Meek, C. & Cooper, G. (1999). A Bayesian Approach to Causal Discovery. In Glymour, C. and G. Cooper, (ed.), Computation, Causation, and Discovery, 141-165. MIT Press.
9. K Korb, Bayesian Artificial Intelligence, CRC Press,2004.
10. S. B. Kotsiantis. : Supervised Machine Learning: A Review of Classification Techniques. In Proceeding of Emerging artificial Intelligence Applications in Computer Engineering: Real Word AI Systems with Applications in eHealth, HCI, Information Retrieval and Pervasive Technologies, 2007.
11. Claudio Bettini, Oliver Brdiczka, Karen Henricksen, Jadwiga Indulska, Daniela Nicklas, Anand Ranganathan, and Daniele Riboni, " A survey of context modelling and reasoning techniques", Pervasive Mobile Computing 6,( 2), April 2010, 161-180.
12. Francesco Chiti, Romano Fantacci, Francesco Archetti, Enza Messina, and Daniele Toscani, "An integrated communications framework for context aware continuous monitoring with body sensor networks", IEEE Journal of Selected Areas in Communication, 27(4) (May 2009), 379-386.
13. A. C and Y. PS, "A framework for clustering uncertain data streams," in Proceedings of IEEE 24rd International Conference on Data Engineering, 2008, pp. 150-159.
14. J. Han and M. Kamber, Data mining: Concepts and Techniques, 2nd ed., Morgan Kaufmann, 2009.
15. W. W. Cohen, "Fast effective rule induction," in Proc. of the 12th Intl. Conf. on Machine Learning, 1995, pp. 115-123.