Improving Classification in Bayesian Networks using Structural Learning
Naïve Bayes classifiers are simple probabilistic
classifiers. Classification extracts patterns by using data file with a set
of labeled training examples and is currently one of the most
significant areas in data mining. However, Naïve Bayes assumes the
independence among the features. Structural learning among the
features thus helps in the classification problem. In this study, the use
of structural learning in Bayesian Network is proposed to be applied
where there are relationships between the features when using the
Naïve Bayes. The improvement in the classification using structural
learning is shown if there exist relationship between the features or
when they are not independent.
[1] A. Darwiche, Modelling and Reasoning with Bayesian Networks.
Cambridge University Press, Los Angeles, 2009.
[2] J. Pearl, Probabilistic Reasoning in Intelligent Systems: networks of
plausible inference. Morgan Kaufmann, 1988.
[3] M. M. Mazid, S. B M. Shawkat Ali and K. S. Tickle, Input space
reduction for Rule Based Classification. WSEAS Transactions on
Information Science and Applications. 2010, Vol. 7, Issue 6, pp. 749-
759.
[4] J. R. Quinlan, C4. 5: programs for machine learning. San Mateo, CA:
Morgan Kaufmann, 2003
[5] I. H. Witten and E. Frank, Generating accurate rule sets without global
optimization, in Proceedings of the Fifteenth International
Conference, San Francisco, CA, 1998.
[6] V. N. Vapnik, The nature of statistical learning theory: Springer
Verlag, Heidelberg, DE, 1995.
[7] S. L. Ang, H. C. Ong, and H. C. Low, Criterion in selecting the
clustering algorithm in Radial Basis Functional Link Nets WSEAS
Transactions on Systems. 2008, Vol. 11, Issue 7, pp. 1290-1299.
[8] R. Agrawal and R. Srikant, Fast algorithms for mining association rules,
in The 20th Int'l Conference on Very Large Databases, Santiago,
Chile, 1994.
[9] J. Cheng and R. Greiner, Learning Bayesian Belief Network Classifiers:
Algorithms and System, Proceedings of the 14th Biennial Conference
of the Canadian Society on Computational Studies of Intelligence:
Advances in Artificial Intelligence 2001, pp. 141 - 151.
[10] A. Almonayyes, Multiple Explanations Driven Naïve Bayes Classifier,
Journal of Universal Computer Science 2006, Vol. 12, Issue 2, pp. 127-
139.
[11] N. Friedman, D. Geiger and M. Goldszmidt, Bayesian Network
Classifiers, Machine Learning, Vol. 29, 1997, pp. 131-163.
[12] Siavash Asadi Ghajarloo. Mining Implicit Knowledge to Predict
Political Risk by Providing Novel Framework with using Bayesian
Network. World Academy of Science, Engineering and Technology.
2011, Vol. 74, pp. 656-663.
[13] M. G. Madden, On the classification performance of TAN and general
Bayesian networks Knowledge-Based Systems, 2009, Vol. 22, Issue 7,
pp. 489-495.
[14] K. M. Al-Aidaroos, A. A. Bakar, and Z. Othman, Naive Bayes variants
in classification learning, International Conference on Information
Retrieval and Knowledge Management 2010, pp. 276-281.
[15] P. Sebastiani, M. M. Abad, and M. F. Ramoni, Bayesian Networks.
Data Mining and Knowledge Discovery Handbook, Eds. Maimon, O.
and Rokach, L. Part 2, Chapter 10, 175-208, Springer, New York 2010.
[16] M. Scutari, Learning Bayesian Networks with the bnlearn R Package,
Journal of Statistical Software, 2010, Vol. 35, Issue 3, pp. 1-22.
[17] M. Scutari and K. Strimmer, Introduction to Graphical Modelling.
Chapter for the upcoming Handbook of Statistical Systems Biology
Balding, D., Stumpf, M., Girolami, M. eds. 21 pages. 2010.
[18] I. H. Witten and E. Frank, Data Mining: Practical Machine Learning
Tools and Techniques, 2nd edition Morgan Kaufmann, San Francisco,
2005.
[19] G. F. Cooper and E. A. Herskovits, Bayesian method for the induction
of probabilistic networks from data. Machine Learning, 1992, Vol. 9,
pp. 309-347.
[20] A. Frank, and A. Asuncion, UCIMachine Learning Repository
[http://archive.ics.uci.edu/ml], (Irvine, CA: University of California,
School of Information and Computer Science, 2010).
[21] G. Yingyu, L. Chunping and Qin,Y. Study on Factors of Floating
Women-s Income in Jiangsu Province Based on Bayesian Networks.
Advances in Neural Network Research and Applications, Lecture Notes
in Electrical Engineering 2010, Vol. 67, Issue 9, pp. 819-827.
[22] R Development Core Team. A Language and Enviroment for Statistical
Computing. [http://www.R-project.org]. R Foundation for Statistical
Computing, Vienna, Austria, 201.
[1] A. Darwiche, Modelling and Reasoning with Bayesian Networks.
Cambridge University Press, Los Angeles, 2009.
[2] J. Pearl, Probabilistic Reasoning in Intelligent Systems: networks of
plausible inference. Morgan Kaufmann, 1988.
[3] M. M. Mazid, S. B M. Shawkat Ali and K. S. Tickle, Input space
reduction for Rule Based Classification. WSEAS Transactions on
Information Science and Applications. 2010, Vol. 7, Issue 6, pp. 749-
759.
[4] J. R. Quinlan, C4. 5: programs for machine learning. San Mateo, CA:
Morgan Kaufmann, 2003
[5] I. H. Witten and E. Frank, Generating accurate rule sets without global
optimization, in Proceedings of the Fifteenth International
Conference, San Francisco, CA, 1998.
[6] V. N. Vapnik, The nature of statistical learning theory: Springer
Verlag, Heidelberg, DE, 1995.
[7] S. L. Ang, H. C. Ong, and H. C. Low, Criterion in selecting the
clustering algorithm in Radial Basis Functional Link Nets WSEAS
Transactions on Systems. 2008, Vol. 11, Issue 7, pp. 1290-1299.
[8] R. Agrawal and R. Srikant, Fast algorithms for mining association rules,
in The 20th Int'l Conference on Very Large Databases, Santiago,
Chile, 1994.
[9] J. Cheng and R. Greiner, Learning Bayesian Belief Network Classifiers:
Algorithms and System, Proceedings of the 14th Biennial Conference
of the Canadian Society on Computational Studies of Intelligence:
Advances in Artificial Intelligence 2001, pp. 141 - 151.
[10] A. Almonayyes, Multiple Explanations Driven Naïve Bayes Classifier,
Journal of Universal Computer Science 2006, Vol. 12, Issue 2, pp. 127-
139.
[11] N. Friedman, D. Geiger and M. Goldszmidt, Bayesian Network
Classifiers, Machine Learning, Vol. 29, 1997, pp. 131-163.
[12] Siavash Asadi Ghajarloo. Mining Implicit Knowledge to Predict
Political Risk by Providing Novel Framework with using Bayesian
Network. World Academy of Science, Engineering and Technology.
2011, Vol. 74, pp. 656-663.
[13] M. G. Madden, On the classification performance of TAN and general
Bayesian networks Knowledge-Based Systems, 2009, Vol. 22, Issue 7,
pp. 489-495.
[14] K. M. Al-Aidaroos, A. A. Bakar, and Z. Othman, Naive Bayes variants
in classification learning, International Conference on Information
Retrieval and Knowledge Management 2010, pp. 276-281.
[15] P. Sebastiani, M. M. Abad, and M. F. Ramoni, Bayesian Networks.
Data Mining and Knowledge Discovery Handbook, Eds. Maimon, O.
and Rokach, L. Part 2, Chapter 10, 175-208, Springer, New York 2010.
[16] M. Scutari, Learning Bayesian Networks with the bnlearn R Package,
Journal of Statistical Software, 2010, Vol. 35, Issue 3, pp. 1-22.
[17] M. Scutari and K. Strimmer, Introduction to Graphical Modelling.
Chapter for the upcoming Handbook of Statistical Systems Biology
Balding, D., Stumpf, M., Girolami, M. eds. 21 pages. 2010.
[18] I. H. Witten and E. Frank, Data Mining: Practical Machine Learning
Tools and Techniques, 2nd edition Morgan Kaufmann, San Francisco,
2005.
[19] G. F. Cooper and E. A. Herskovits, Bayesian method for the induction
of probabilistic networks from data. Machine Learning, 1992, Vol. 9,
pp. 309-347.
[20] A. Frank, and A. Asuncion, UCIMachine Learning Repository
[http://archive.ics.uci.edu/ml], (Irvine, CA: University of California,
School of Information and Computer Science, 2010).
[21] G. Yingyu, L. Chunping and Qin,Y. Study on Factors of Floating
Women-s Income in Jiangsu Province Based on Bayesian Networks.
Advances in Neural Network Research and Applications, Lecture Notes
in Electrical Engineering 2010, Vol. 67, Issue 9, pp. 819-827.
[22] R Development Core Team. A Language and Enviroment for Statistical
Computing. [http://www.R-project.org]. R Foundation for Statistical
Computing, Vienna, Austria, 201.
@article{"International Journal of Engineering, Mathematical and Physical Sciences:64044", author = "Hong Choon Ong", title = "Improving Classification in Bayesian Networks using Structural Learning", abstract = "Naïve Bayes classifiers are simple probabilistic
classifiers. Classification extracts patterns by using data file with a set
of labeled training examples and is currently one of the most
significant areas in data mining. However, Naïve Bayes assumes the
independence among the features. Structural learning among the
features thus helps in the classification problem. In this study, the use
of structural learning in Bayesian Network is proposed to be applied
where there are relationships between the features when using the
Naïve Bayes. The improvement in the classification using structural
learning is shown if there exist relationship between the features or
when they are not independent.", keywords = "Bayesian Network, Classification, Naïve Bayes,Structural Learning.", volume = "5", number = "3", pages = "494-5", }
