跳至主要内容

Extending Qualitative Probabilistic Network with Mutual Information Weights

Read  full  paper  at:
http://www.scirp.org/journal/PaperInformation.aspx?PaperID=53761#.VNHQqCzQrzE

Author(s) 
 
ABSTRACT
Bayesian network (BN) is a well-accepted framework for representing and inferring uncertain knowledge. As the qualitative abstraction of BN, qualitative probabilistic network (QPN) is introduced for probabilistic inferences in a qualitative way. With much higher efficiency of inferences, QPNs are more suitable for real-time applications than BNs. However, the high abstraction level brings some inference conflicts and tends to pose a major obstacle to their applications. In order to eliminate the inference conflicts of QPN, in this paper, we begin by extending the QPN by adding a mutual-information-based weight (MI weight) to each qualitative influence in the QPN. The extended QPN is called MI-QPN. After obtaining the MI weights from the corresponding BN, we discuss the symmetry, transitivity and composition properties of the qualitative influences. Then we extend the general inference algorithm to implement the conflict-free inferences of MI-QPN. The feasibility of our method is verified by the results of the experiment.
 
Cite this paper
Yue, K. , Wang, F. , Wei, M. and Liu, W. (2015) Extending Qualitative Probabilistic Network with Mutual Information Weights. International Journal of Intelligence Science, 5, 133-144. doi: 10.4236/ijis.2015.53012.
 
References
[1]Liu, W.Y., Li, W.H. and Yue, K. (2007) Intelligent Data Analysis. Science Press, Beijing.
 
[2]Pearl, J. (1988) Probabilistic Reasoning in Intelligent Systems: Network of Plausible Inference. Morgan Kaufmann, San Mateo.
 
[3]Li, W.H., Liu, W.Y. and Yue, K. (2008) Recovering the Global Structure from Multiple Local Bayesian Networks. International Journal on Artificial Intelligence Tools, 17, 1067-1088.
http://dx.doi.org/10.1142/S0218213008004308
 
[4]Cooper, G.F. (1990) The Computational Complexity of Probabilistic Inference Using Bayesian Belief Networks. Artificial Intelligence, 42, 393-405.
http://dx.doi.org/10.1016/0004-3702(90)90060-D
 
[5]Dagum, P. and Luby, M. (1993) Approximating Probabilistic Inference in Bayesian Belief Networks Is NP-Hard. Artificial Intelligence, 60, 141-153.
http://dx.doi.org/10.1016/0004-3702(93)90036-B
 
[6]Wellman, M.P. (1990) Fundamental Concepts of Qualitative Probabilistic Networks. Artificial Intelligence, 44, 257-303.
http://dx.doi.org/10.1016/0004-3702(90)90026-V
 
[7]Bolt, J.H., Renooij, S. and Van der Gaag, L.C. (2003) Upgrading Ambiguous Signs in QPNs. Proceedings of the 19th Conference in Uncertainty in Artificial Intelligence, Acapulco, 7-10 August 2003, 73-80.
 
[8]Druzdzel, M.J. and Henrion, M. (1993) Efficient Reasoning in Qualitative Probabilistic Networks. Proceedings of the 11th National Conference on Artificial Intelligence, Washington DC, 11-15 July 1993, 548-553.
 
[9]Lv, Y.L. and Liao, S.Z. (2011) Ambiguity Reduction Based on Qualitative Mutual Information in Qualitative Probabilistic Networks. Pattern Recognition and Artificial Intelligence, 24, 123-129.
 
[10]Parsons, S. (1995) Refining Reasoning in Qualitative Probabilistic Networks. Proceedings of the 11th Conference on Uncertainty in Artificial Intelligence, Montreal, 18-20 August 1995, 427-434.
 
[11]Renooij, S., Parsons, S. and Pardieck, P. (2003) Using Kappas as Indicators of Strength in Qualitative Probabilistic Networks. Proceedings of European Conferences on Symbolic and Quantitative Approaches to Reasoning with Uncertainty, Aalborg, 2-5 July 2003, 87-99.
 
[12]Renooij, S. and Van der Gaag, L.C. (1999) Enhancing QPNs for Trade-Off Resolution. Proceedings of the 15th Conference on Uncertainty in Artificial Intelligence, Stockholm, 30 July-1 August 1999, 559-566.
 
[13]Renooij, S., Van der Gaag, L.C. and Parsons, S. (2002) Context-Specific Sign-Propagation in Qualitative Probabilistic Networks. Artificial Intelligence, 140, 207-230.
http://dx.doi.org/10.1016/S0004-3702(02)00247-3
 
[14]Renooij, S., Van der Gaag, L.C., Parsons, S. and Green, S. (2000) Pivotal Pruning of Trade-Off in QPNs. Proceedings of the 16th Conference in Uncertainty in Artificial Intelligence, Stanford, 30 June-3 July 2000, 515-522.
 
[15]Renooij, S. and Van der Gaag, L.C. (2002) From Qualitative to Quantitative Probabilistic Network. Proceedings of the 18th Conference on Uncertainty in Artificial Intelligence, Edmonton, 1-4 August 2002, 422-429.
 
[16]Yue, K., Yao, Y., Li, J. and Liu, W.Y. (2010) Qualitative Probabilistic Networks with Reduced Ambiguities. Applied Intelligence, 33, 159-178.
http://dx.doi.org/10.1007/s10489-008-0156-5
 
[17]Cover, T.M. and Thomas, J.A. (1993) Elements of Information Theory. John Wiley & Sons, Inc., Hoboken.
 
[18]Shannon, C.E. and Weaver, W. (1949) The Mathematical Theory of Communication. University of Illinois Press, Champaign.
 
[19]Chen, X.W., Anantha, G. and Lin, X.T. (2008) Improving Bayesian Network Structure Learning with Mutual Information-Based Node Ordering in the K2 Algorithm. IEEE Transactions on Knowledge and Data Engineering, 20, 628-640.
http://dx.doi.org/10.1109/TKDE.2007.190732
 
[20]De Campos, L.M. (2006) A Scoring Function for Learning Bayesian Networks Based on Mutual Information and Conditional Independence Tests. Journal of Machine Learning Research, 7, 2149-2187.
 
[21]Nicholson, A.E. and Jitnah, N. (1998) Using Mutual Information to Determine Relevance in Bayesian Networks. Proceedings of the 5th Pacific Rim International Conference on Artificial Intelligence, Singapore, 22-27 November 1998, 399-410.
 
[22]Ibrahim, Z.M., Ngom, A. and Tawk, A.Y. (2011) Using Qualitative Probability in Reverse-Engineering Gene Regulatory Networks. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 8, 326-334.
http://dx.doi.org/10.1109/TCBB.2010.98
 
[23]Liu, W.Y., Yue, K., Liu, S.X. and Sun, Y.B. (2008) Qualitative-Probabilistic-Network-Based Modeling of Temporal Causalities and Its Application to Feedback Loop Identification. Information Sciences, 178, 1803-1824.
http://dx.doi.org/10.1016/j.ins.2007.11.021
 
[24]Yue, K., Qian, W.H., Fu, X.D., Li, J. and Liu, W.Y. (2014) Qualitative-Probabilistic-Network-Based Fusion of Time-Series Uncertain Knowledge. Soft Computing, Published Online.
http://dx.doi.org/10.1007/s00500-014-1381-y.
 
[25]De Campos, C.P. and Cozman, F.G. (2013) Complexity of Inferences in Polytree-Shaped Semi-Qualitative Probabilistic Networks. Proceedings of the 27th AAAI Conference on Artificial Intelligence, Bellevue, 14-18 July 2013, 217-223.
 
[26]Yue, K., Liu, W. and Yue, M. (2011) Quantifying Influences in the Qualitative Probabilistic Network with Interval Probability Parameters. Applied Soft Computing, 11, 1135-1143.
http://dx.doi.org/10.1016/j.asoc.2010.02.013
 
[27]Shafer, G. (1986) The Combination of Evidence. International Journal of Intelligent Systems, 1, 155-179.
http://dx.doi.org/10.1002/int.4550010302                                                                             eww150204lx
 
[28]Norsys Software Corp. (2007) Netica 3.17 Bayesian Network Software from Norsys. http://www.norsys.com  
标签：

评论

发表评论

此博客中的热门博文

Does Immigration Promote the Investment of the Monopolistic Firm?

In the present paper, we examine the effect of increasing uncertainty of immigrants’ growth on the optimal timing of investment of a firm that has a monopolistic power over the labor market. It is revealed that when the uncertainty of immigrants’ growth is more than a threshold level, increasing uncertainty of immigrants’ growth accelerates the optimal timing of firms’ investment and enhances the economic growth, even if the uncertainty of immigrants’ growth is formulated by the geometric Brownian motion, which is in sharp contrast to the standard result that an increase in the uncertainty postpones the optimal timing. With an increase in the immigrants over the past ten years, workforces in the host countries have been growing significantly to the extent that the immigrants represent 70% of the increase in the workforce in Europe, and 47% in the United States as OECD indicates. In the present paper, we attempted to investigate the effect of increased uncertainty caused by the growi...
发表评论
阅读全文

Education Policy Implementation: A Mechanism for Enhancing Primary Education Development in Zanzibar

Education is one of the fundamental rights of individuals; therefore, the government of a country needs to develop and strengthen educational policy and quality as well as to ensure that everyone has equal access to basic education. The improvement of access and quality of education in the world is becoming as an essential factor in development, whereas the basic education (primary school), is acknowledged as a foundation of the higher educational development for every country. To fulfill this goal, governments introduce several policies and procedures; however, it requires some reforms and participation from the politician, policymakers, and other stakeholders to re-examine educational policy so that it can lead to multiplication and betterment of the reforms. Educational reforms actually focus on accountability. A positive educational development and reform is very challenging and needs more effort and strategy on how to use and utilize the resources effectively as such it can achie...
发表评论
阅读全文