Abstract
Missing or incomplete data sets are a common problem in data mining. To deal with structured data of this type, the interpretation of attribute values is a contributing factor to the resulting accuracy as well as complexity of the rule sets induced. In this paper, lost values and “do not care” conditions are studied as a representation for the missing values. Further study is conducted with global and saturated approximations, two new types of probabilistic approximations. These approaches are combined to produce four primary data mining experiments; rule induction with two types of approximations and two interpretations of missing attribute values. The main objective of this work is to compare the complexity of the induced rule sets by the four approaches to find the lowest complexity of rules. This is a complement to previous research where experimental evidence show that none of the four approaches induces rules with the lowest error in all scenarios, and it depends on the data set being mined. The result of this paper’s experiments in complexity show that using the “do not care” condition provides simpler rules sets than the lost value interpretation of missing attribute values. Furthermore, there is not statistically significant differences in complexity between using global or saturated probabilistic approximations.
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References
Clark, P.G., Gao, C., Grzymala-Busse, J.W., Mroczek, T., Niemiec, R.: A comparison of concept and global probabilistic approximations based on mining incomplete data. In: Proceedings of ICIST 2018, the International Conference on Information and Software Technologies, pp. 324–335 (2018)
Clark, P.G., Grzymala-Busse, J.W., Mroczek, T., Niemiec, R.: A comparison of global and saturated probabilistic approximations using characteristic sets in mining incomplete data. In: Proceedings of the Eight International Conference on Intelligent Systems and Applications, pp. 10–15 (2019)
Clark, P.G., Grzymala-Busse, J.W., Mroczek, T., Niemiec, R.: Rule set complexity in mining incomplete data using global and saturated probabilistic approximations. In: Proceedings of the 25-th International Conference on Information and Software Technologies, pp. 451–462 (2019)
Grzymala-Busse, J.W.: LERS—a system for learning from examples based on rough sets. In: Slowinski, R. (ed.) Intelligent Decision Support. Handbook of Applications and Advances of the Rough Set Theory, pp. 3–18. Kluwer Academic Publishers, Dordrecht (1992)
Grzymala-Busse, J.W.: Generalized parameterized approximations. In: Proceedings of the 6-th International Conference on Rough Sets and Knowledge Technology, pp. 136–145 (2011)
Grzymala-Busse, J.W., Clark, P.G., Kuehnhausen, M.: Generalized probabilistic approximations of incomplete data. Int. J. Approximate Reason. 132, 180–196 (2014)
Grzymala-Busse, J.W., Rzasa, W.: Local and global approximations for incomplete data. In: Proceedings of the Fifth International Conference on Rough Sets and Current Trends in Computing, pp. 244–253 (2006)
Grzymala-Busse, J.W., Rzasa, W.: Local and global approximations for incomplete data. Trans. Rough Sets 8, 21–34 (2008)
Grzymala-Busse, J.W., Ziarko, W.: Data mining based on rough sets. In: Wang, J. (ed.) Data Mining: Opportunities and Challenges, pp. 142–173. Idea Group Publ, Hershey (2003)
Pawlak, Z., Skowron, A.: Rough sets: some extensions. Inf. Sci. 177, 28–40 (2007)
Pawlak, Z., Wong, S.K.M., Ziarko, W.: Rough sets: probabilistic versus deterministic approach. Int. J. Man-Mach. Stud. 29, 81–95 (1988)
Ślȩzak, D., Ziarko, W.: The investigation of the Bayesian rough set model. Int. J. Approximate Reason. 40, 81–91 (2005)
Wong, S.K.M., Ziarko, W.: INFER—an adaptive decision support system based on the probabilistic approximate classification. In: Proceedings of the 6-th International Workshop on Expert Systems and their Applications, pp. 713–726 (1986)
Yao, Y.Y.: Probabilistic rough set approximations. Int. J. Approximate Reason. 49, 255–271 (2008)
Yao, Y.Y., Wong, S.K.M.: A decision theoretic framework for approximate concepts. Int. J. Man-Mach. Stud. 37, 793–809 (1992)
Ziarko, W.: Variable precision rough set model. J. Comput. Syst. Sci. 46(1), 39–59 (1993)
Ziarko, W.: Probabilistic approach to rough sets. Int. J. Approximate Reason. 49, 272–284 (2008)
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Clark, P.G., Grzymala-Busse, J.W., Hippe, Z.S., Mroczek, T., Niemiec, R. (2021). Complexity of Rule Sets Induced from Data with Many Lost Values and “Do Not Care” Conditions. In: Abraham, A., Siarry, P., Ma, K., Kaklauskas, A. (eds) Intelligent Systems Design and Applications. ISDA 2019. Advances in Intelligent Systems and Computing, vol 1181. Springer, Cham. https://doi.org/10.1007/978-3-030-49342-4_36
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DOI: https://doi.org/10.1007/978-3-030-49342-4_36
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