Predictive models for dengue outbreak using multiple rulebase classifiers

Proceedings of the 2011 International Conference on Electrical Engineering and Informatics, ICEEI 2011

Volumn , Issue , 2011, Pages

  Bakar, Azuraliza Abu ^a   Kefli, Zuriyah ^a   Abdullah, Salwani ^a   Sahani, Mazrura ^a  

^a UNIVERSITI KEBANGSAAN MALAYSIA   (Malaysia)

Author keywords

associative classification; decision tree; Dengue outbreak; multiple rule based classifier; Naive Bayes; rough set

Indexed keywords

ASSOCIATIVE CLASSIFICATION; DENGUE OUTBREAK; NAIVE BAYES; ROUGH SET; RULE-BASED CLASSIFIER;

DECISION TREES; ELECTRICAL ENGINEERING; FORECASTING; INFORMATION SCIENCE; PLANT EXTRACTS; PREDICTIVE CONTROL SYSTEMS; ROUGH SET THEORY;

MODELS;

EID: 80054051671     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICEEI.2011.6021830     Document Type: Conference Paper

References (28)

1. Husin, N.A. 2008. "Back propagation neural network and non-linear regression models for dengue outbreak," M. Sc. thesis, Universiti Teknologi Malaysia, Johor, Malaysia, Nov. 2008.
2. Poovaneswari, S. 1993. "Dengue situation in Malaysia," Malaysia J Pathol, vol. 15(1), pp. 3-7, June. 1993.
3. Gubler, J. 1997. "Epidemic Dengue/Dengue Haemorrhagic Fever: A Global Public Health Problem in the 21st Century," Dengue Bulletin, vol. 21, pp. 1-120.
4. Er, A.C., Rosli, M.H., Asmahani A., Mohamad Naim M.R., Harsuzilawati M. 2010. "Spatial mapping of Dengue incidence: A case study in Hulu Langat district, Selangor, Malaysia," International Journal of Human and Social Sciene, vol. 15(1), pp. 410-414.
5. Muto R. 1998. Summary of dengue situation in WHO Wester Pacific Region. Dengue Bulletin 22. [Cited 08 Feb 2011]. Available : http://www.searo.who.int/ EN/Section10/Section332/Section520.htm
6. Teng and Singh. 2001. "Epidemiology and new initiatives in the prevention and control of dengue in Malaysia," Dengue Bulletin, vol. 25, pp. 7-14.
7. Qin,B., Xia, Y., Prabhakar, S. and Tu,Y. 2009. "A rule-based classification algorithm for uncertain data," in IEEE International Conference on Data Engineering. ICDE'09, pp. 1633-1640.
8. Briem, G.J., Benediktsson, J.A. & Sveinsson, J.R. 2002. "Multiple classifiers applied to multisource remote sensing data," IDBIS, vol .40, pp. 2291-2299.
9. Cufoglu, A., Lohi, M. & Madani, K. 2008. "Classification accuracy performance of Naïve Bayesian (NB), Bayesian Networks (BN), Lazy Learning of Bayesian Rules(LBR) and Instance-Based Learner (IB1) - comparative study," in Conf. ICCES'08, p. 210-215.
10. Berzal, F., Cubero, J.C., Sánchez, D. & Serrano,J.M. 2002. "ART: Hybrid Classification Model," IEEE Transactions on Geoscience and Remote Sensing, pp. 1-17.
11. Aslandogan, Y.A. & Mahajani, G.A. 2004. "Evidence combination in medical data mining," in Proc. ITCC'04.
12. Ma, A., Sethi, I.K., Schwartz, M. & Page, U.S. 2004. "Using multiple classifiers to improve classification of post-operative complications in cardiac surgery," MEDINFO, pp. 1737.
13. Stefanowski, J. & Wilk, S. "Combining rough sets and rule based classifiers for handling imbalanced data".
14. Chitra, A. & Uma, S. 2010. "A An ensemble model of multiple classifiers for time series prediction," International Journal of Computer Theory and Engineering., vol. 2, pp. 454-458.
15. Shin, J. & Han,D. 2010. "Multi-Classifier for WLAN fingerprint-based positioning system," in Proc. WCE'10, vol. 1, ISBN: 978-988-17012- 9-9.
16. Lee, L.H., Wan, C.H., Yong, T.F. & Kok,H.M. 2010. "A review of nearest neighbour-support vector machine hybrid classification models," Journal of Applied Science, vol. 10 (17), pp. 1841-1858.
17. Korayem,M, Hamad, W.A. & Mostafa, K. 2010. "A hybrid genetic algorithm and artificial immune system for informative gene selection," IJCSNS International Journal of Computer Science and Network Security, vol. 10, pp. 76-83.
18. Thabtah, F. 2007. "A review of Associative Classification Mining," The Knowledge Engineering Review, vol. 22.1, pp. 37-65.
19. Han, J & Kamber, M. 2006. Data Mining: Concepts and Techniques, 2nd ed., Morgan Kaufmann Publishers. San Francisco, United States of America: Elsevier.
20. Hassanien, A.E., Mohamed E. Abdelhafez, and Hala S. Own.2008. "Rough sets data analysis in knowledge discovery: A case of Kuwaiti diabetic children patients,"Advances in Fuzzy Systems, vol. 2008, pp. 1-13.
21. Pawlak, Z., Grzymala-Bussr, J., Slowinski, R. & Ziarko, W. 1995. "Rough Set",Communication of the ACM, vol. 38, no.11.
22. A. Ohrn, "ROSETTA technical Reference Manual," Knowledge System Group, Norwegian University of Science and Technology, Throndheim, Norway.
23. P. R. Pal, R.C. Jain. 2010. "Combinatorial approach of associative classification," Int. J. Advanced Networking and Applications, vol. 2 (1), pp. 470-474.
24. Soni,S. & Vyas, O.P. 2010. "Using associative classifiers for predictive analysis in health care data mining," International Journal of Computer Applications, vol. 4, pp. 33-37.
25. Li, W., Han, J. & Pei,J. "CMAR: Accurate and efficient classification based on multiple class-association rules".
26. Yin, X. and Han, J. "CPAR: Classification based on predictive association rules," SIAM, pp. 331-335.
27. Li,J. 2007. "Rough set based rule evaluations and their applications," Phd. Computer Science. University of Waterloo, Ontario.
28. Zhao,Y., Zhang, C. & Cao, L. 2009."Post-mining of Association Rules: Techniques for Effective Knowledge Extraction" Information Science Reference; 1st edition.