Abstract
Rough Sets theory is widely used as a method for estimating and/or inducing the knowledge structure of if-then rules from a decision table after a reduct of the table. The concept of a reduct is that of constructing a decision table by necessary and sufficient condition attributes to induce the rules. This paper retests the reduct by the conventional methods by the use of simulation datasets after summarizing the reduct briefly and points out several problems of their methods. Then, a new reduct method based on a statistical viewpoint is proposed and confirmed to be valid by applying it to the simulation datasets. The new reduct method is incorporated into STRIM (Statistical Test Rule Induction Method), and plays an effective role for the rule induction. The STRIM including the reduct method is also applied for a UCI dataset and shows to be very useful and effective for estimating if-then rules hidden behind the decision table of interest.
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
Pawlak, Z.: Rough sets. Int. J. Inf. Comput. Sci. 11(5), 341–356 (1982)
Grzymala-Busse, J.W.: LERS — a system for learning from examples based on rough sets. In: Słowiński, R. (ed.) Intelligent Decision Support — Handbook of Applications and Advances of the Rough Sets Theory. Theory and Decision Library, vol. 11, pp. 3–18. Kluwer Academic Publishers, Amsterdam (1992)
Skowron, A., Rauser, C.M.: The discernibility matrix and functions in information systems. In: Słowiński, R. (ed.) Intelligent Decision Support — Handbook of Applications and Advances of the Rough Sets Theory. Theory and Decision Library, vol. 11, pp. 331–362. Kluwer Academic Publishers, Amsterdam (1992)
Pawlak, Z.: Rough set fundamentals; KFIS Autumn Coference Tutorial, pp. 1–32 (1996)
Ślęzak, D.: Various approaches to reasoning with frequency based decision reducts: a survey. In: Polkowski, L., Tsumoto, S., Lin, T.Y. (eds.) Rough Set Method and Applications, vol. 56, pp. 235–285. Physical-Verlag, Heidelberg (2000)
Bao, Y.G., Du, X.Y., Deng, M.G., Ishii, N.: An efficient method for computing all reducts. Trans. Jpn. Soc. Artif. Intell. 19(3), 166–173 (2004)
Jia, X., Shang, L., Zhou, Z., Yao, Y.: Generalized attribute reduct in rough set theory. Knowl.-Based Syst. 91, 204–218 (2016). Elsevier
Matsubayashi, T., Kato, Y., Saeki, T.: A new rule induction method from a decision table using a statistical test. In: Li, T., Nguyen, H.S., Wang, G., Grzymala-Busse, J., Janicki, R., Hassanien, A.E., Yu, H. (eds.) RSKT 2012. LNCS (LNAI), vol. 7414, pp. 81–90. Springer, Heidelberg (2012). doi:10.1007/978-3-642-31900-6_11
Kato, Y., Saeki, T., Mizuno, S.: Studies on the necessary data size for rule induction by STRIM. In: Lingras, P., Wolski, M., Cornelis, C., Mitra, S., Wasilewski, P. (eds.) RSKT 2013. LNCS (LNAI), vol. 8171, pp. 213–220. Springer, Heidelberg (2013). doi:10.1007/978-3-642-41299-8_20
Kato, Y., Saeki, T., Mizuno, S.: Considerations on rule induction procedures by STRIM and their relationship to VPRS. In: Kryszkiewicz, M., Cornelis, C., Ciucci, D., Medina-Moreno, J., Motoda, H., Raś, Z.W. (eds.) RSEISP 2014. LNCS (LNAI), vol. 8537, pp. 198–208. Springer, Cham (2014). doi:10.1007/978-3-319-08729-0_19
Kato, Y., Saeki, T., Mizuno, S.: Proposal of a statistical test rule induction method by use of the decision table. Appl. Soft Compt. 28, 160–166 (2015). Elsevier
Asunction, A., Newman, D.J.: UCI machine learning repository. University of California, School of Information and Computer Science, Irvine (2007). http://www.ics.edu/~mlearn/MlRepository.html
Walpole, R.E., Myers, R.H., Myers, S.L., Ye, K.: Probability and Statistics for Engineers and Scientists, 8th edn., pp. 374–377. Pearson Prentice Hall, New Jersey (2007)
Walpole, R.E., Myers, R.H., Myers, S.L., Ye, K.: Probability and Statistics for Engineers and Scientists, 8th edn., pp. 361–364. Pearson Prentice Hall, New Jersey (2007)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Fei, J., Saeki, T., Kato, Y. (2017). Proposal for a New Reduct Method for Decision Tables and an Improved STRIM. In: Tan, Y., Takagi, H., Shi, Y. (eds) Data Mining and Big Data. DMBD 2017. Lecture Notes in Computer Science(), vol 10387. Springer, Cham. https://doi.org/10.1007/978-3-319-61845-6_37
Download citation
DOI: https://doi.org/10.1007/978-3-319-61845-6_37
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-61844-9
Online ISBN: 978-3-319-61845-6
eBook Packages: Computer ScienceComputer Science (R0)