Abstract
Data mining is a widely acceptable method on mining knowledge from large databases, and classification is an important technique in this research field. A naïve Bayesian classifier is a simple but effective probabilistic classifier, which has been widely used in classification. It is commonly thought to assume that the probability of each attribute belonging to a given class value is independent of all other attributes in the naïve Bayesian classifier; however, there are lots of contexts where the dependencies between attributes are complex and should thus be considered carefully. It is an important technique that constructing a classifier using specific patterns based on “attribute-value” pairs in lots of researchers’ work, and the classification result will be impacted by dependencies between these specific patterns meanwhile. In this paper, a lazy one-dependence classification algorithm based on selective patterns is proposed, which utilizes both the patterns’ discrimination and dependencies between attributes. The classification accuracy benefits from mining and employing patterns which own high discrimination, and building the one-dependence relationship between attributes in a proper way. Through an exhaustive experimental evaluation, it shows that the proposed algorithm is competitive in accuracy with the state-of-the-art classification techniques on datasets from the UCI repository.
Supported by Beijing Natural Science Foundation (4182052), and National Natural Science Foundation of China (61672086, 61771058).
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References
Domingos, P., Pazzani, M.: Beyond independence: conditions for the optimality of the simple Bayesian classifier. In: Saitta, L. (ed.) Proceedings of the 13th ICML, pp. 105–112. Morgan Kaufmann, San Francisco (1996)
Friedman, N., Goldszmidt, M.: Building classifiers using Bayesian networks. In: Proceedings of the 13th National Conference on Artificial Intelligence (AAAI 1996), pp. 1277–1284. AAAI Press, Menlo Park (1996)
Webb, G., Boughton, J., Wang, Z.: Not so naïve Bayes: aggregating one-dependence estimators. Mach. Learn. 58(1), 5–24 (2005)
Chen, S., Martínez, A.M., Webb, G.I.: Highly scalable attribute selection for averaged one-dependence estimators. In: Tseng, V.S., Ho, T.B., Zhou, Z.-H., Chen, A.L.P., Kao, H.-Y. (eds.) PAKDD 2014. LNCS (LNAI), vol. 8444, pp. 86–97. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-06605-9_8
Chen, S., Martínez, A.M., Webb, G.I., Wang, L.: Selective AnDE for large data learning: a low-bias memory constrained approach. Knowl. Inf. Syst. 50(2), 475–503 (2017)
Yu, L., Jiang, L., Wang, D., Zhang, L.: Attribute value weighted average of one-dependence estimators. Entropy 19(9), 501 (2017)
Dong, G., Zhang, X., Wong, L., Li, J.: CAEP: classification by aggregating emerging patterns. In: Arikawa, S., Furukawa, K. (eds.) DS 1999. LNCS (LNAI), vol. 1721, pp. 30–42. Springer, Heidelberg (1999). https://doi.org/10.1007/3-540-46846-3_4
Fan, H., Ramamohanarao, K.: A Bayesian approach to use emerging patterns for classification. In: Schewe, K., Zhou, X. (eds.) Proceedings of the 14th Australasian Database Conference, pp. 39–48. ACS Press, Adelaide, Australia (2003)
Li, J., Dong, G., Ramamohanarao, K.: Making use of the most expressive jumping emerging patterns for classification. Knowl. Inf. Syst. 3(2), 131–145 (2001)
Dong, G., Li, J.: Efficient mining of emerging patterns: discovering trends and differences. In: Proceedings of the 5th ACM SIGKDD International Conference on KDD, pp. 43–52. ACM Press, New York (1999)
Blake, C., Merz, C.: UCI repository of machine learning databases. http://archive.ics.uci.edu/ml/index.html. Accessed 1 June 2018
Fayyad, U.M., Irani, K.B.: Multi-interval discretization of continuous valued attributes for classification learning. In: Bajcsy, R. (ed.) Proceedings of the 13th International Joint Conference on Artificial Intelligence, pp. 1022–1027. Morgan Kaufmann, San Mateo, CA (1993)
Demšar, J.: Statistical comparisons of classifiers over multiple data sets. J. Mach. Learn. Res. 7(1), 1–30 (2006)
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Ju, Z., Wang, Z., Wang, S. (2018). A Lazy One-Dependence Classification Algorithm Based on Selective Patterns. In: Geng, X., Kang, BH. (eds) PRICAI 2018: Trends in Artificial Intelligence. PRICAI 2018. Lecture Notes in Computer Science(), vol 11013. Springer, Cham. https://doi.org/10.1007/978-3-319-97310-4_13
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DOI: https://doi.org/10.1007/978-3-319-97310-4_13
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