Abstract
Determining the causal structure of a domain is frequently a key task in the area of Data Mining and Knowledge Discovery. This paper introduces ensemble learning into linear causal model discovery, then examines several algorithms based on different ensemble strategies including Bagging, Adaboost and GASEN. Experimental results show that (1) Ensemble discovery algorithm can achieve an improved result compared with individual causal discovery algorithm in terms of accuracy; (2) Among all examined ensemble discovery algorithms, BWV algorithm which uses a simple Bagging strategy works excellently compared to other more sophisticated ensemble strategies; (3) Ensemble method can also improve the stability of parameter estimation. In addition, Ensemble discovery algorithm is amenable to parallel and distributed processing, which is important for data mining in large data sets.
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References
Bollen, K.: Structural Equations with Latent Variables. Wiley, New York (1989)
Wallace, C., Korb, K., Dai, H.: Causal discovery via MML. In: Proceedings of the 13th International Conference on Machine Learning (ICML 1996), pp. 516–524 (1996)
Wallace, C., Boulton, D.: An information measure for classification. Computer Journal 11, 185–194 (1968)
Dai, H., Korb, K., Wallace, C., Wu, X.: A study of causal discovery with small samples and weak links. In: Proceedings of the 15th International Joint Conference On Artificial Intelligence IJCAI 1997, pp. 1304–1309. Morgan Kaufmann Publishers, Inc, San Francisco (1997)
Dai, H., Li, G.: An improved approach for the discovery of causal models via MML. In: Proceedings of The 6th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2002), Taiwan, pp. 304–315 (2002)
Li, G., Dai, H., Tu, Y.: Linear causal model discovery using MML criterion. In: Proceedings of 2002 IEEE International Conference on Data Mining, Maebashi City, Japan, pp. 274–281. IEEE Computer Society, Los Alamitos (2002)
Dai, H., Li, G., Tu, Y.: An empirical study of encoding schemes and search strategies in discovering causal networks. In: Elomaa, T., Mannila, H., Toivonen, H. (eds.) ECML 2002. LNCS (LNAI), vol. 2430, pp. 48–59. Springer, Heidelberg (2002)
Breiman, L.: Bagging predictors. Machine Learning 24, 123–140 (1996)
Efron, B., Tibshirani, R.: An introduction to the bootstrap. Chapman and Hall, New York (1993)
Zhou, Z.H., Wu, J., Tang, W.: Enselbling neural networks: Many could be better than all. Artificial Intelligence 137, 993–1001 (2002)
Freund, Y., Schapire, R.: A decision-theoretic generalization of on-line learning and an application to boosting. In: Proceedings of EuroCOLT 1994, Barcelona, Spain, pp. 23–37 (1995)
Thollard, F., Sebban, M., Ezequel, P.: Boosting density function estimators. In: Elomaa, T., Mannila, H., Toivonen, H. (eds.) ECML 2002. LNCS (LNAI), vol. 2430, pp. 431–443. Springer, Heidelberg (2002)
Murphy, K.: The Bayes Net Toolbox for Matlab. Computer Science and Statistics 33, 331–351 (2001)
Houck, C., Joines, J., Kay, M.: A genetic algorithm for function optimization: a matlab implementation. Technical Reports NCSU-IE-TR-95-09, North Carolina State University, Raleigh, NC (1995)
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Li, G., Dai, H. (2005). Study of Ensemble Strategies in Discovering Linear Causal Models. In: Wang, L., Jin, Y. (eds) Fuzzy Systems and Knowledge Discovery. FSKD 2005. Lecture Notes in Computer Science(), vol 3614. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11540007_46
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DOI: https://doi.org/10.1007/11540007_46
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28331-7
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