Abstract
In this paper we present results of novel experiments conducted on 12 data sets with many missing attribute values interpreted as attribute-concept values and “do not care” conditions. In our experiments complexity of rule sets, in terms of the number of rules and the total number of conditions induced from such data, are evaluated. The simpler rule sets are considered better. Our first objective was to check which interpretation of missing attribute values should be used to induce simpler rule sets. There is some evidence that the “do not care” conditions are better. Our secondary objective was to test which of the three probabilistic approximations: singleton, subset or concept, used for rule induction should be used to induce simpler rule sets. The best choice is the subset probabilistic approximation and the singleton probabilistic approximation is the worst choice.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Clark, P.G., Grzymala-Busse, J.W.: Experiments on probabilistic approximations. In: Proceedings of the 2011 IEEE International Conference on Granular Computing, pp. 144–149 (2011)
Clark, P.G., Grzymala-Busse, J.W.: Complexity of rule sets induced from incomplete data sets with attribute-concept values and and “do not care” conditions. In: Proceedings of the Third International Conference on Data Management Technologies and Applications, pp. 56–63 (2014)
Clark, P.G., Grzymala-Busse, J.W.: Mining incomplete data with attribute-concept values and “do not care” conditions. In: Polycarpou, M., de Carvalho, A.C.P.L.F., Pan, J.-S., Woźniak, M., Quintian, H., Corchado, E. (eds.) HAIS 2014. LNCS (LNAI), vol. 8480, pp. 156–167. Springer, Heidelberg (2014). doi:10.1007/978-3-319-07617-1_14
Clark, P.G., Grzymala-Busse, J.W.: Mining incomplete data with many attribute-concept values and do not care conditions. In: Proceedings of the IEEE International Conference on Big Data, pp. 1597–1602 (2015)
Clark, P.G., Grzymala-Busse, J.W.: On the number of rules and conditions in mining data with attribute-concept values and “do not care” conditions. In: Kryszkiewicz, M., Bandyopadhyay, S., Rybinski, H., Pal, S.K. (eds.) PReMI 2015. LNCS, vol. 9124, pp. 13–22. Springer, Heidelberg (2015). doi:10.1007/978-3-319-19941-2_2
Clark, P.G., Grzymala-Busse, J.W., Kuehnhausen, M.: Mining incomplete data with many missing attribute values. a comparison of probabilistic and rough set approaches. In: Proceedings of the Second International Conference on Intelligent Systems and Applications, pp. 12–17 (2013)
Clark, P.G., Grzymala-Busse, J.W., Rzasa, W.: Mining incomplete data with singleton, subset and concept approximations. Inf. Sci. 280, 368–384 (2014)
Grzymala-Busse, J.W.: On the unknown attribute values in learning from examples. In: Proceedings of the 6th International Symposium on Methodologies for Intelligent Systems, pp. 368–377 (1991)
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.: A new version of the rule induction system LERS. Fundamenta Informaticae 31, 27–39 (1997)
Grzymala-Busse, J.W.: MLEM2: a new algorithm for rule induction from imperfect data. In: Proceedings of the 9th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, pp. 243–250 (2002)
Grzymala-Busse, J.W.: Rough set strategies to data with missing attribute values. In: Notes of the Workshop on Foundations and New Directions of Data Mining, in Conjunction with the Third International Conference on Data Mining, pp. 56–63 (2003)
Grzymala-Busse, J.W.: Data with missing attribute values: generalization of indiscernibility relation and rule induction. Trans. Rough Sets 1, 78–95 (2004)
Grzymala-Busse, J.W.: Three approaches to missing attribute values—a rough set perspective. In: Proceedings of the Workshop on Foundation of Data Mining, in Conjunction with the Fourth IEEE International Conference on Data Mining, pp. 55–62 (2004)
Grzymala-Busse, J.W.: Generalized parameterized approximations. In: Yao, J.T., Ramanna, S., Wang, G., Suraj, Z. (eds.) RSKT 2011. LNCS (LNAI), vol. 6954, pp. 136–145. Springer, Heidelberg (2011). doi:10.1007/978-3-642-24425-4_20
Grzymala-Busse, J.W., Rzasa, W.: Definability and other properties of approximations for generalized indiscernibility relations. Trans. Rough Sets 11, 14–39 (2010)
Kryszkiewicz, M.: Rules in incomplete information systems. Inf. Sci. 113(3–4), 271–292 (1999)
Pawlak, Z.: Rough sets. Int. J. Comput. Inform. Sci. 11, 341–356 (1982)
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)
Stefanowski, J., Tsoukias, A.: Incomplete information tables and rough classification. Comput. Intell. 17(3), 545–566 (2001)
Yao, Y.Y.: Probabilistic rough set approximations. Int. J. Approximate Reasoning 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.: Probabilistic approach to rough sets. Int. J. Approximate Reasoning 49, 272–284 (2008)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing AG
About this paper
Cite this paper
Clark, P.G., Gao, C., Grzymala-Busse, J.W. (2016). Rule Set Complexity for Incomplete Data Sets with Many Attribute-Concept Values and “Do Not Care” Conditions. In: Flores, V., et al. Rough Sets. IJCRS 2016. Lecture Notes in Computer Science(), vol 9920. Springer, Cham. https://doi.org/10.1007/978-3-319-47160-0_6
Download citation
DOI: https://doi.org/10.1007/978-3-319-47160-0_6
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-47159-4
Online ISBN: 978-3-319-47160-0
eBook Packages: Computer ScienceComputer Science (R0)