Abstract
Bayesian networks are popular in the classification literature. The simplest kind of Bayesian network, i.e. naïve Bayesian network, has gained the interest of many researchers because of quick learning and inferring. However, when there are lots of classes to be inferred from a similar set of evidences, one may prefer to have a united network. In this paper we present a new method for merging naïve networks in order achieve a complete network and study the effect of this merging. The proposed method reduces the burden of learning a complete network. A simple measure is also introduced to assess the stability of the results after the combination of classifiers. The merging method is applied to the image classification problem. The results indicate that in addition to the reduced computation burden for learning a complete network, the total precision is increased and the precision alteration for each individual class is estimable using the measure.
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References
Jain, A.K., Duin, R.P.W., Mao, J.: Statistical pattern recognition: A review. IEEE Trans on pattern recognition and machine intelligence 22(1), 4–37 (2000)
Heckerman, D.: A tutorial on learning with Bayesian networks. Technical report, Microsoft Research (1996)
Vailaya, A., Figueiredo, M., Jain, A., Zhang, H.J.: Image Classification for Content-Based Indexing. IEEE Transactions on Image Processing 10(1), 117–130 (2001)
Naphade, M.R., Huang, T.S.: A probabilistic framework for semantic video indexing, filtering and retrieval. IEEE Trans. on multimedia 3(1), 141–151 (2001)
Lerner, B., Malka, R.: Learning Bayesian Networks for Cytogenetic Image Classification. In: Proc. 18th ACM conf. on pattern recognition, vol. 2, pp. 772–775 (2006)
Huang, K., King, I., Lyu, M.R.: Finite mixture model of bounded semi-naive Bayesian network classifier. In: Kaynak, O., Alpaydın, E., Oja, E., Xu, L. (eds.) ICANN 2003 and ICONIP 2003. LNCS, vol. 2714, pp. 115–122. Springer, Heidelberg (2003)
Yu, X., Zheng, Z., Wu, J., Zhang, X., Wu, F.: Texture classification of aerial image based on Bayesian networks with hidden nodes. In: Advances in computation and intelligence. Springer, Heidelberg (2007)
Chickering, D.M., Heckerman, D.: Efficient approximation of the marginal likelihood of Bayesian networks with hidden variables. Technical report, Microsoft Research (1997)
Shachter, R.D., Keneley, D.: Gaussian influence diagrams. Management science 35 (1989)
Degroot, M.H.: Optimal Statistical Decisions. McGraw-Hill, New York (1970)
Pearl, J.: Probabilistic Reasoning in Intelligent Systems. Morgan Kaufmann, San Francisco (1988)
Bouman, C.A., Shapiro, M.: A multiscale random field model for Bayesian image segmentation. IEEE Trans. on image processing 3(2), 162–174 (1994)
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Shariat, S., Rabiee, H.R., Khansari, M. (2008). Inferring a Bayesian Network for Content-Based Image Classification. In: Sarbazi-Azad, H., Parhami, B., Miremadi, SG., Hessabi, S. (eds) Advances in Computer Science and Engineering. CSICC 2008. Communications in Computer and Information Science, vol 6. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-89985-3_26
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DOI: https://doi.org/10.1007/978-3-540-89985-3_26
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-89984-6
Online ISBN: 978-3-540-89985-3
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