Abstract
A framework of learning a new form of rules from incomplete data is introduced so that a user can easily identify attributes with or without missing values in a rule. Two levels of measurement are assigned to a rule. An algorithm for two-phase rule induction is presented. Instead of filling in missing attribute values before or during the process of rule induction, we divide rule induction into two phases. In the first phase, rules and partial rules are induced based on non-missing values. In the second phase, partial rules are modified and refined by filling in some missing values. Such rules truthfully reflect the knowledge embedded in the incomplete data. The study not only presents a new view of rule induction from incomplete data, but also provides a practical solution.
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
Preview
Unable to display preview. Download preview PDF.
References
Greco, S., Matarazzo, B., Slowinski, R.: Handling Missing Values in Rough SetAnalysis of Multi-attribute and Multi-criteria Decision Problems. In: Zhong, N., Skowron, A., Ohsuga, S. (eds.) RSFDGrC 1999. LNCS (LNAI), vol. 1711, pp. 146–157. Springer, Heidelberg (1999)
Grzymala-Busse, J.W., Hu, M.: A Comparison of Several Approches to Missing Attribute Values in Data Mining. In: Ziarko, W., Yao, Y. (eds.) RSCTC 2000. LNCS (LNAI), vol. 2005, pp. 378–385. Springer, Heidelberg (2001)
Grzymala-Busse, J.W., Grzymala-Busse, W.J.: An Experimental Comparison of Three Rough Set Approaches to Missing Attribute Values. In: Peters, J.F., Skowron, A., Düntsch, I., Grzymała-Busse, J.W., Orłowska, E., Polkowski, L. (eds.) Transactions on Rough Sets VI. LNCS, vol. 4374, pp. 31–50. Springer, Heidelberg (2007)
Grzymala-Busse, J.W.: Data with Missing Attribute Values: Generalization of Indiscernibility Relation and Rule Induction. In: Peters, J.F., Skowron, A., Grzymała-Busse, J.W., Kostek, B.z., Świniarski, R.W., Szczuka, M. (eds.) Transactions on Rough Sets I. LNCS, vol. 3100, pp. 78–95. Springer, Heidelberg (2004)
Kryszkiewicz, M.: Rough Set Approach to Incomplete Information Systems. Information Sciences 112, 39–49 (1998)
Kryszkiewicz, M.: Rules in Incomplete Information Systems. Information Sciences 113, 271–292 (1999)
Mitchell, T.M.: Generalization as Search. Artificial Intelligence 18, 203–226 (1982)
Polkowski, L., Artiemjew, P.: Granular Classifiers and Missing Values. In: Proc. of ICCI 2007, pp. 186–194 (2007)
Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann, San Mateo (1993)
Stefamowski, J., Tsoukeas, A.: On the Extension of Rough Sets under Incomplete Information. Int. J. of Intel. Sys. 16, 29–38 (1999)
Yao, J.T., Yao, Y.Y.: Induction of Classification Rules by Granular Computing. In: Alpigini, J.J., Peters, J.F., Skowron, A., Zhong, N. (eds.) RSCTC 2002. LNCS (LNAI), vol. 2475, pp. 331–338. Springer, Heidelberg (2002)
Yao, Y.Y.: Concept Formation and Learning: A Cognitive Informatics Perspective. In: Proc. of the ICCI 2004, pp. 42–51 (2004)
Yao, Y.Y., Zhong, N.: An Analysis of Quantitative Measures Associated with Rules. In: Kapoor, S., Prasad, S. (eds.) FST TCS 2000. LNCS, vol. 1974, pp. 479–488. Springer, Heidelberg (2000)
Zhang, S.C., Qin, Z.X., Ling, C.X., Sheng, S.L.: Missing is Useful: Missing Values in Cost-sensitive Decision Trees. IEEE Trans. on Know. and Data Eng. 17, 1689–1693 (2005)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Li, H., Yao, Y., Zhou, X., Huang, B. (2008). Two-Phase Rule Induction from Incomplete Data. In: Wang, G., Li, T., Grzymala-Busse, J.W., Miao, D., Skowron, A., Yao, Y. (eds) Rough Sets and Knowledge Technology. RSKT 2008. Lecture Notes in Computer Science(), vol 5009. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-79721-0_12
Download citation
DOI: https://doi.org/10.1007/978-3-540-79721-0_12
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-79720-3
Online ISBN: 978-3-540-79721-0
eBook Packages: Computer ScienceComputer Science (R0)