Supervised classification with Bayesian networks: A review on models and applications

Intelligent Data Analysis for Real-Life Applications: Theory and Practice

Volumn , Issue , 2012, Pages 72-102

  Flores, M Julia ^a,b   Gámez, José A ^a,b   Martínez, Ana M ^a,b  

^a INSTITUTO DE INVESTIGACIÓN EN INFORMÁTICA DE ALBACETE   (Spain)

^b Intelligent Systems and Data Mining research group   (Spain)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 84898110224     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.4018/978-1-4666-1806-0.ch005     Document Type: Chapter

References (95)

1. Aguilera, P. A., Fernández, A., Reche, F., & Rumí, R. (2010). Hybrid Bayesian network classifiers: Application to species distribution models. Environmental Modelling & Software, 25(12), 1630-1639. doi:10.1016/j.envsoft.2010.04.016
2. Ammar, K., McKee, M., & Kaluarachchi, J. (2011). Bayesian method for groundwater quality monitoring network analysis. Journal of Water Resources Planning and Management, 137(1), 51-61. doi:10.1061/(ASCE)WR.19435452.0000043
3. Antal, P., Fannes, G., Timmerman, D., Moreau, Y., & DeMoor, B. (2003). Bayesian applications of belief networks and multilayer perceptrons for ovarian tumor classification with rejection. Artificial Intelligence in Medicine, 29, 39-60. doi:10.1016/S0933-3657(03)00053-8
4. Armañanzas, R. (2009). Consensus policies to solve bioinformatic problems through Bayesian network classifiers and estimation of distribution algorithms. Department of Computer Science and Artificial Intelligence.
5. Armañanzas, R., Inza, I., & Larrañaga, P. (2008). Detecting reliable gene interactions by a hierarchy of Bayesian network classifiers. Computer Methods and Programs in Biomedicine, 91(2), 110-121. doi:10.1016/j.cmpb.2008.02.010
6. Avilés-Arriaga, H. H., & Sucar, L. E. (2002). Dynamic Bayesian networks for visual recognition of dynamic gestures. Journal of Intelligent and Fuzzy Systems, 12(3-4), 243-250.
7. Avilés-Arriaga, H. H.,Sucar, L. E., &Mendoza, C. E. (2006). Visual recognition of similar gestures. Proceedings of the 18th International Conference on Pattern Recognition - ICPR'06.
8. Axelson, D. E., Standal, I. B., Martinez, I., & Aursand, M. (2009). Classification of wild and farmed salmon using Bayesian belief networks and gas chromatography-derived fatty acid distributions. Journal of Agricultural and Food Chemistry, 57(17), 7634-7639. doi:10.1021/jf9013235
9. Bielza, C., Li, G., & Larrañaga, P. (2011). Multi-dimensional classification with Bayesian networks. International Journal of Approximate Reasoning, 52(6). doi:10.1016/j.ijar.2011.01.007
10. Borchani, H., Bielza, C., & Larrañaga, P. (2010). Learning CB-decomposable multi-dimensional Bayesian network classifiers. Proceedings of the 5th European Workshop on Probabilistic Graphical Models (PGM'10).
11. Boyles, S. F. D., & Waller, S. T. (2007). A naïve Bayesian classifer for incident duration prediction. TRB - Transportation Research Board - Annual Meeting, Washington D.C.
12. Bressan, G. M., Oliveira, V. A., Hruschka, E. R. Jr, & Nicoletti, M. C. (2009). Using Bayesian networks with rule extraction to infer the risk of weed infestation in a corn-crop. Engineering Applications of Artificial Intelligence, 22(4-5), 579-592. doi:10.1016/j.engappai.2009.03.006
13. Chmielewski, M. R., & Jerzy. (1996). Global discretization of continuous attributes as preprocessing for machine learning. International Journal of Approximate Reasoning, 15(4), 319-331. doi:10.1016/S0888-613X(96)00074-6
14. Chow, C. I., & Liu, C. N. (1968). Approximating discrete probability distributions with dependence trees. IEEE Transactions on Information Theory, 14, 462-467. doi:10.1109/TIT.1968.1054142
15. Cooper, G. F., & Herskovits, E. (1992). A Bayesian method for the induction of probabilistic networks from data. Machine Learning, 9, 309-347. doi:10.1007/BF00994110
16. Cowell, R. G., Dawid, P. A., Lauritzen, S. L., & Spiegelhalter, D. J. (2003). Probabilistic networks and expert systems (information science and statistics). Springer.
17. de Waal, P. R., & van der Gaag, L. C. (2007). Inference and learning in multi-dimensional Bayesian network classifiers. European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty (Vol. 4724, pp. 501-511).
18. Deaton, A. (1997). The analysis of household surveys: A microeconometric approach to development policy. World Bank. Baltimore, MD: Johns Hopkins University Press.
19. DeGroot, M. H. (1970). Optimal statistical decisions. New York, NY: McGraw-Hill.
20. DeGroot, M. H. (2004). Optimal statistical decisions. Wiley-Interscience. doi:10.1002/0471729000
21. Domingos, P., & Pazzani, M. J. (1996). Beyond independence: Conditions for the optimality of the simple Bayesian classifier. International Conference on Machine Learning.
22. Duda, R. O., Hart, P. E., & Stork, D. G. (1973). Pattern classification and scene analysis. New York, NY: Wiley.
23. Fenton, N. E., & Neil, M. (2007). Managing risk in the modern world: Bayesian networks and the applications. London, UK: London Mathematical Society, Knowledge Transfer Report.
24. Fernandes, J. A., Irigoien, X., Goikoetxea, N., Lozano, J. A., Inza, I., & Perez, A. (2010). Fish recruitment prediction, using robust supervised classification methods. Ecological Modelling, 221(2), 338-352. doi:10.1016/j.ecolmodel.2009.09.020
25. Fernandez, A., & Salmeron, A. (2008). Bayes-Chess: A computer chess program based on Bayesian networks. Pattern Recognition Letters, 29(8), 1154-1159. doi:10.1016/j.patrec.2007.06.013
26. Fernández, A., & Salmerón, A. (2008). Extension of Bayesian network classifiers to regression problems. Proceedings of the 11th Ibero-American conference on AI: Advances in Artificial Intelligence.
27. Flesch, I., Fernández, A., & Salmerón, A. (2007). Incremental supervised classification for the MTE distribution: A preliminary study. Actas de Simposio de Inteligencia Computacional, SICO'2007.
28. Flores, J. L., Inza, I., & Larrañaga, P. (2007). Wrapper discretization by means of estimation of distribution algorithms. Intelligent Data Analysis, 11(5), 525-545.
29. Flores, M. J., Gámez, J. A., Martínez, A. M., & Puerta, J. M. (2009a). GAODE and HAODE: Two proposals based on AODE to deal with continuous variables. International Conference on Machine Learning - ICML.
30. Flores, M. J., Gámez, J. A., Martínez, A. M., & Puerta, J. M. (2009b). HODE: Hidden onedependence estimator. European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty.
31. Flores, M. J., Gámez, J. A., Martínez, A. M., & Puerta, J. M. (2011). Handling numeric attributes when comparing Bayesian classifiers: Does the discretization method matter? Applied Intelligence, 34(3), 372-385. doi:10.1007/s10489011-0286-z
32. Friedman, N., Geiger, D., & Goldszmidt, M. (1997). Bayesian network classifiers. Machine Learning, 29, 131-163. doi:10.1023/A:1007465528199
33. Gámez, J. A. (2004). Abductive inference in Bayesian networks: A review. Advances in Bayesian Networks (pp. 101-120). Springer Verlag.
34. Gámez, J. A., Mateo, J. L., Nielsen, T. D., & Puerta, J. M. (2008). Robust classification using mixtures of dependency networks. Proceedings of the Fourth European Workshop on Probabilistic Graphical Models (PGM08).
35. Gámez, J. A., Mateo, J. L., & Puerta, J. M. (2006). Dependency networks based classifiers: Learning models by using independence. Proceedings of the 3rd European Workshop on Probabilistic Graphical Models.
36. Gámez, J. A., Mateo, J. L., & Puerta, J. M. (2007). Learning Bayesian classifiers from dependency network classifiers. Proceedings of the 8th International Conference on Adaptive and Natural Computing Algorithms (ICANNGA-07).
37. Geiger, D., & Heckerman, D. (1996). Knowledge representation and inference in similarity networks and Bayesian multinets. Artificial Intelligence, 82, 45-74. doi:10.1016/0004-3702(95)00014-3
38. Heckerman, D., Chickering, D. M., Meek, C., Rounthwaite, R., & Kadie, C. (2001). Dependency networks for inference, collaborative filtering, and data visualization. Journal of Machine Learning Research, 1, 49-75.
39. Hruschka, E. R., Jr., Hruschka, E. R., & Ebecken, N. F. F. (2005). Applying Bayesian networks for meteorological data mining. SGAI International Conference on Artificial Intelligence.
40. Jaeger, M. (2004). Probabilistic decision graphs - Combining verification and AI techniques for probabilistic inference. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 12(Supplement-1), 19-42.
41. Jiang, L., & Zhang, H. (2006). Weightily averaged one-dependence estimators. Proceedings of the 9th Pacific Rim International Conference on Artificial Intelligence.
42. Jiang, L., Zhang, H., Cai, Z., & Su, J. (2005). Learning tree augmented naïve Bayes for ranking. Database Systems for Advanced Applications, 3453, 992-992. doi:10.1007/11408079_63
43. John, G. H., & Langley, P. (1995). Estimating continuous distributions in Bayesian classifiers. Conference on Uncertainty in Artificial Intelligence.
44. Kammerdiner, A. R., Gupal, A. M., & Pardalos, P. M. (2007). Application of Bayesian networks and data mining to biomedical problems. AIP Conference Proceedings.
45. Keogh, E., & Pazzani, M. (1999). Learning augmented Bayesian classifiers: A comparison of distribution-based and classification-based approaches. Proceedings of the 7th International Workshop on AI and Statistics (pp. 225-230).
46. Kononenko, I. (1991). Semi-naive Bayesian classifiers. EWSL, 1991, 206-219. doi:10.1007/BFb0017015
47. Korb, K. E., & Nicholson, A. E. (2010). Bayesian artificial intelligence, 2nd ed. Chapman & Hall/CRC Computer Science & Data Analysis.
48. Langley, P., Iba, W., & Thompson, K. (1992). An analysis of Bayesian classifiers. Proceedings of the Tenth Annual Conference on Artificial Intelligence.
49. Langley, P., & Sage, S. (1994). Induction of selective Bayesian classifiers. Tenth Conference on Uncertainty in Artificial Intelligence.
50. Larrañaga, P. (2010). Multi-label classification. International Journal of Data Warehousing and Mining, 3(3), 1-13.
51. Lauritzen, S. L. (1992). Propagation of probabilities, means and variances in mixed graphical association models. Journal of the American Statistical Association, 87, 1098-1108.
52. Lauritzen, S. L., & Jensen, F. (2001). Stable local computation with conditional Gaussian distributions. Statistics and Computing, 11(2), 191-203. doi:10.1023/A:1008935617754
53. Lauritzen, S. L., & Wermuth, N. (1998). Graphical models for associations between variables, some of which are qualitative and some quantitative. Annals of Statistics, 17(1), 31-57. doi:10.1214/aos/1176347003
54. Link, W. A., & Barker, R. J. (2010). Bayesian inference: With ecological applications. Elsevier.
55. Liu, H., Hussain, F., Tan, C. L., & Dash, M. (2002). Discretization: An enabling technique. Data Mining and Knowledge Discovery, 6(4), 393-423. doi:10.1023/A:1016304305535
56. Lucas, P. (2004). Restricted Bayesian network structure learning. Studies in Fuzziness and Soft Computing, 49, 217-232.
57. Martínez, A. M., Webb, G. I., Flores, M. J., & Gámez, J. A. (2011). Non-disjoint discretization for aggregating one-dependence estimator classifiers. (Submitted): University of Castilla-La Mancha, Computing Systems Department.
58. Martínez, M., & Sucar, L. E. (2008). Learning dynamic naïve Bayesian classifiers. Proceedings of the Twenty-First International Florida Artificial Intelligence Research Society Conference (FLAIRS) (pp. 655-659).
59. Moral, S., Rumí, R., & Salmerón, A. (2001). Mixtures of truncated exponentials in hybrid Bayesian networks. Paper presented at the ECSQARU '01: 6th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty.
60. Moral, S., Rumí, R., & Salmerón, A. (2003). Approximating conditional MTE distributions by means of mixed trees. Paper presented at the ECSQARU.
61. Morales, D. A., Bengoetxea, E., & Larrañaga, P. (2008). Selection of human embryos for transfer by Bayesian classifiers. Computers in Biology and Medicine, 38, 1177-1186. doi:10.1016/jcompbiomed.2008.09.002
62. Nielsen, J. D., Rumí, R., & Salmerón, A. (2009). Supervised classification using probabilistic decision graphs. Computational Statistics & Data Analysis, 53(4), 1299-1311. doi:10.1016/j.csda.2008.11.003
63. Olesen, K. G. (1993). Causal probabilistic networks with both discrete and continuous variables. IEEE Transactions on Pattern Analysis and Machine Intelligence, 15(3), 275-279. doi:10.1109/34.204909
64. Ordoñez, C., Matias, J. M., Rivas, T., & Bastante, F. G. (2009). Reforestation planning using Bayesian networks. Environmental Modelling & Software, 24(11), 1285-1292. doi:10.1016/j.envsoft.2009.05.009
65. Ortigosa-Hernandez, J., Rodriguez, J. D., Alzate, L., Lucania, M., Inza, I., & Lozano, J. A. (2011). Approaching sentiment analysis by using semi-supervised learning of multidimensional classifiers. Department of Computer Science and Artificial Intelligence. University of the Basque Country.
66. Palacios-Alonso, M. A., Brizuela, C. A., & Sucar, L. E. (2010). Evolutionary learning of dynamic naïve Bayesian classifiers. Journal of Automated Reasoning, 45(1), 21-37. doi:10.1007/s10817009-9130-0
67. Park, M.-H., & Stenstrom, M. K. (2008). Classifying environmentally significant urban land uses with satellite imagery. Journal of Environmental Management, 86(1), 181-192. doi:10.1016/j.jenvman.2006.12.010
68. Pavlenko, T., & Chernyak, O. (2010). Credit risk modeling using Bayesian networks. International Journal of Intelligent Systems, 25, 326-344.
69. Pazzani, M. J. (1996). Searching for dependencies in Bayesian classifiers. Learning from Data: Artificial Intelligence and Statistics, V, 239-248.
70. Pérez, A., Larrañaga, P., & Inza, I. (2006). Supervised classification with conditional Gaussian networks: Increasing the structure complexity from naïve Bayes. International Journal of Approximate Reasoning, 43(1), 1-25. doi:10.1016/j.ijar.2006.01.002
71. Pérez, A., Larrañaga, P., & Inza, I. (2009). Bayesian classifiers based on kernel density estimation: Flexible classifiers. International Journal of Approximate Reasoning, 50, 341-362. doi:10.1016/j.ijar.2008.08.008
72. Porwal, A., Carranza, E. J. M., & Hale, M. (2006). Bayesian network classifiers for mineral potential mapping. Computers & Geosciences, 32, 1-16. doi:10.1016/j.cageo.2005.03.018
73. Qazi, M., Fung, G., Krishnan, S., Rosales, R., Steck, H., Rao, R. B., et al. (2007). Automated heart wall motion abnormality detection from ultrasound images using Bayesian networks. Proceedings of the 20th International Joint Conference on Artifical Intelligence.
74. Quinlan, J. R. (1986). Induction of decision trees. Machine Learning, 1(1), 81-106. doi:10.1007/BF00116251
75. Rehg, J. M., & Murphy, K. P. (1999). Vision-based speaker detection using Bayesian networks. In Workshop on Perceptual User-Interfaces.
76. Rodríguez, J. D., & Lozano, J. A. (2008). Multiobjective learning of multi-dimensional Bayesian classifiers. Proceedings of 8th International Conference on Hybrid Intelligent Systems (HIS 2008).
77. Rumí, R., Salmerón, A., & Moral, S. (2006). Estimating mixtures of truncated exponentials in hybrid Bayesian networks. TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, 15(2), 397-421.
78. Sahami, M. (1996). Learning limited dependence Bayesian classifiers. Proceedings of the 2nd International Conference on Knowledge Discovery in Databases (pp. 335-338).
79. Sahami, M., Dumais, S., Heckerman, D., amp; Horvitz, E. (1998). A Bayesian approach to filtering junk e-mail. AAAI Workshop on Learning for Text Categorization.
80. Shachter, R. D., & Kenley, C. R. (1989). Gaussian influence diagrams. Management Science, 35(5), 527-550. doi:10.1287/mnsc.35.5.527
81. Silverman, B. W. (1986). Density estimation for statistics and data analysis. Chapman & Hall/CRC.
82. Su, J., & Zhang, H. (2006). Full Bayesian network classifiers. Proceedings of the 23rd International Conference on Machine Learning.
83. Tsoumakas, G., & Katakis, I. (2007). Multi-label classification: An overview. International Journal of Data Warehousing and Mining, 3(3), 1-13. doi:10.4018/jdwm.2007070101
84. van der Gaag, L. C., & de Waal, P. R. (2006). Multi-dimensional Bayesian network classifiers. Third European Workshop on Probabilistic Graphical Models.
85. Wang, Q., Garrity, G. M., Tiedje, J. M., & Cole, R. J. (2007). Naïve Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Applied and Environmental Microbiology, 73(16), 5261-5267. doi:10.1128/AEM.00062-07
86. Webb, G. I., Boughton, J. R., & Wang, Z. (2005). Not so naïve Bayes: Aggregating one-dependence estimators. Machine Learning, 58(1), 5-24. doi:10.1007/s10994-005-4258-6
87. Yang, Y., & Webb, G. I. (2009). Discretization for naïve-Bayes learning: Managing discretization bias and variance. Machine Learning, 74(1), 39-74. doi:10.1007/s10994-008-5083-5
88. Yinh, Y. (2003). Discretization for naïve-Bayes learning. PhD. Thesis, Monash University.
89. Zaffalon, M. (2005). Credible classification for environmental problems. Environmental Modelling & Software, 20(8), 1003-1012. doi:10.1016/j.envsoft.2004.10.006
90. Zaragoza, J., Sucar, E., & Morales, E. (2011). A two-step method to learn multidimensional Bayesian network classifiers based on mutual information measures. Twenty-Fourth International Florida Artificial Intelligence Research Society Conference - FLAIRS24.
91. Zhang, H., Jiang, L., & Su, J. (2005). Hidden naïve Bayes. Proceedings of the 20th National Conference on Artificial intelligence - Volume 2 (pp. 919-924). AAAI Press.
92. Zhang, H., & Lu, Y. (2002). Learning Bayesian network classifiers from data with missing values. TENCON '02 Proceedings, IEEE Region 10 Conference on Computers, Communications, Control and Power Engineering.
93. Zheng, F., & Webb, G. I. (2005). A comparative study of semi-naïve Bayes methods in classification learning. Proceedings of 4th Australasian Data Mining Conference (AusDM05.
94. Zheng, Z., & Webb, G. I. (2000). Lazy learning of Bayesian rules. Machine Learning, 41(1), 53-84. doi:10.1023/A:1007613203719
95. Zhong, S., Martínez, A. M., Nielsen, T. D., & Langseth, H. (2010). Towards a more expressive model for dynamic classification. Proceedings of the Twenty-Third International Florida Artificial Intelligence Research Society Conference (FLAIRS).