Abstract
The dynamic nature of data streams leads to a number of computational and mining challenges. In such environments, Bayesian network structure learning incrementally by revising existing structure could be an efficient way to save time and memory constraints. The local search methods for structure learning outperforms to deal with high dimensional domains. The major task in local search methods is to identify the local structure around the target variable i.e. parent children (PC). In this paper we transformed the local structure identification part of MMHC algorithm into an incremental fashion by using heuristics proposed by reducing the search space. We applied incremental hill-climbing to learn a set of candidate- parent-children (CPC) for a target variable. Experimental results and theoretical justification that demonstrate the feasibility of our approach are presented.
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References
Alcobé, J.R.: Incremental hill-climbing search applied to bayesian network structure learning. In: First International Workshop on Knowledge Discovery in Data Streams, KDDS (2004)
Alcobé, J.R.: Incremental methods for bayesian network structure learning. AI Communications 18(1), 61–62 (2005)
Buntine, W.: Theory refinement on bayesian networks. In: Proceedings of the Seventh Conference (1991) on Uncertainty in Artificial Intelligence, pp. 52–60. Morgan Kaufmann Publishers Inc., San Francisco (1991)
de Campos, L.M.: A scoring function for learning bayesian networks based on mutual information and conditional independence tests. J. Mach. Learn. Res. 7, 2149–2187 (2006)
Castillo, G., Gama, J.a.: Adaptive bayesian network classifiers. Intell. Data Anal. 13, 39–59 (2009)
Chickering, D.: Learning bayesian networks is NP-complete. In: Proceedings of AI and Statistics, pp. 121–130 (1995)
Chickering, D., Geiger, D., Heckerman, D.: Learning bayesian networks: Search methods and experimental results. In: Proceedings of Fifth Conference on Artificial Intelligence and Statistics, pp. 112–128 (1995)
Cooper, G.F., Herskovits, E.: A bayesian method for the induction of probabilistic networks from data. Machine Learning 9, 309–347 (1992), doi:10.1007/BF00994110
Friedman, N., Goldszmidt, M.: Sequential update of bayesian network structure. In: Proc. 13th Conference on Uncertainty in Artificial Intelligence (UAI 1997), pp. 165–174. Morgan Kaufmann, San Francisco (1997)
Gama, J.: Knowledge Discovery from Data Streams. CRC Press, Boca Raton (2010)
Lam, W.: Bayesian network refinement via machine learning approach. IEEE Trans. Pattern Anal. Mach. Intell. 20(3), 240–251 (1998)
Lam, W., Bacchus, F.: Using new data to refine a bayesian network. In: Proceedings of the Tenth Conference on Uncertainty in Artificial Intelligence, pp. 383–390. Morgan Kaufmann, San Francisco (1994)
Nielsen, S.H., Nielsen, T.D.: Adapting bayes network structures to non-stationary domains. Int. J. Approx. Reasoning 49(2), 379–397 (2008)
Rodrigues de Morais, S., Aussem, A.: A novel scalable and data efficient feature subset selection algorithm. In: Daelemans, W., Goethals, B., Morik, K. (eds.) ECML PKDD 2008, Part II. LNCS (LNAI), vol. 5212, pp. 298–312. Springer, Heidelberg (2008)
Shi, D., Tan, S.: Incremental learning bayesian network structures efficiently. In: Proc. 11th Int Control Automation Robotics & Vision (ICARCV) Conf., pp. 1719–1724 (2010)
Spirtes, P., Glymour, C., Scheines, R.: Causation, Prediction, and Search, 2nd edn. Adaptive Computation and Machine Learning Series. The MIT Press, Cambridge (2000)
Tsamardinos, I., Aliferis, C.F., Statnikov, A.: Time and sample efficient discovery of Markov blankets and direct causal relations. In: Proceedings of the Ninth ACM SIGKDD international Conference on Knowledge Discovery and Data Mining, KDD 2003, pp. 673–678. ACM, New York (2003)
Tsamardinos, I., Brown, L.E., Aliferis, C.F.: The max-min hill-climbing bayesian network structure learning algorithm. Mach. Learn. 65(1), 31–78 (2006)
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Yasin, A., Leray, P. (2011). iMMPC: A Local Search Approach for Incremental Bayesian Network Structure Learning. In: Gama, J., Bradley, E., Hollmén, J. (eds) Advances in Intelligent Data Analysis X. IDA 2011. Lecture Notes in Computer Science, vol 7014. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24800-9_37
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DOI: https://doi.org/10.1007/978-3-642-24800-9_37
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