Abstract
Application of logic synthesis methods for data mining is discussed. The key concept is to apply the Boolean function complement algorithm for rule induction. The presented results of experiments with large medical databases indicate that the proposed approach significantly improves the efficiency of the rule induction procedure. Compared with the earlier presented, commonly used algorithm, the average rule accuracy has increased by 10% and the rule coverage by 15%, ultimately reaching 81.5% and 97.0%, respectively.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
An, A., Cercone, N.: Rule Quality Measures for Rule Induction Systems: Description and Evaluation. Computational Intelligence 17(3), 409–424 (2001), DOI: 10.1111/0824-7935.00154
Andersen, T., Martinez, T.: Learning and generalization with bounded order rule sets. In: Proc. of 10th Int. Symp. on Computer and Information Sciences. pp. 419–426 (1995)
Borowik, G., Łuba, T.: Fast Algorithm of Attribute Reduction Based on the Complementation of Boolean Function. Klempous, R., Nikodem, J., Jacak, W., Chaczko, Z. (eds.) Advanced Methods and Applications in Computational Intelligence, Topics in Intelligent Engineering and Informatics, vol. 6, pp. 25–41. Springer International Publishing (2014), DOI: 10.1007/978-3-319-01436-4-2
Brayton, R.K., Hachtel, G.D., McMullen, C.T., Sangiovanni-Vincentelli, A.: Logic Minimization Algorithms for VLSI Synthesis. Kluwer Academic Publishers (1984)
Bruha, I.: Quality of decision rules: definitions and classification schemes for multiple rules. Nakhaeizadeh, G., Taylor, C. (eds.) Machine Learning and Statistics, pp. 107–131. Wiley and Sons (1997)
Clark, P., Boswell, R.: Rule induction with CN2: Some recent improvements. Kodratoff, Y. (ed.) Machine Learning — EWSL–91, Lecture Notes in Computer Science, vol. 482, pp. 151–163. Springer Berlin Heidelberg (1991), DOI: 10.1007/BFb0017011
Cohen, W.W.: Fast effective rule induction. In: Proc. of the Twelfth International Conference on Machine Learning. pp. 115–123. Morgan Kaufmann (1995)
Domingos, P.: Rule Induction and Instance-based Learning: A Unified Approach. In: Proceedings of the 14th International Joint Conference on Artificial Intelligence – Volume 2. pp. 1226–1232. IJCAI’95, Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1995)
Džeroski, S., Lavrač, N.: Rule induction and instance-based learning applied in medical diagnosis. Technology and Health Care 4(2), 203–221 (1996)
Grzymala-Busse, J., Wang, A.: Modified algorithms LEM1 and LEM2 for rule induction from data with missing attribute values. In: Proc. of the Fifth International Workshop on Rough Sets and Soft Computing (RSSC’97) at the Third Joint Conference on Information Sciences (JCIS’97). pp. 69–72. Research Triangle Park, NC (Mar 1997)
Komorowski, J., Pawlak, Z., Polkowski, L., Skowron, A.: Rough sets: A tutorial (1999)
Maimon, O., Rokach, L.: Data Mining and Knowledge Discovery Handbook. Springer, 2 edn. (2010)
Mańkowski, M.: Decision rule generalization using complement of Boolean function (in Polish). B.Sc. dissertation, Warsaw University of Technology (2014)
Mitchell, T.: Machine Learning. Mac-Graw Hill, Boston (1997)
Papadimitriou, C.H.: Computational complexity. Academic Internet Publ. (2007)
Pawlak, Z.: Rough Sets. Theoretical Aspects of Reasoning about Data. Kluwer Academic Publishers (1991)
Skowron, A., Rauszer, C.: The Discernibility Matrices and Functions in Information Systems. Słowiński, R. (ed.) Intelligent Decision Support, Theory and Decision Library, vol. 11, pp. 331–362. Springer Netherlands (1992), DOI: 10.1007/978-94-015-7975-9-21
Smyth, P., Goodman, R.: Rule induction using information theory. Knowledge Discovery in Databases. AAAI/MIT Press (1991)
Stefanowski, J.: Rule induction algorithms for knowledge discovery (in Polish). Monograph Series, 361, Poznan University of Technology Publishing House, Poznan (2001)
Stefanowski, J., Vanderpooten, D.: A General Two-Stage Approach to Inducing Rules from Examples. Ziarko, W.P. (ed.) Rough Sets, Fuzzy Sets and Knowledge Discovery, pp. 317–325. Workshops in Computing, Springer London (1994), DOI: 10.1007/978-1-4471-3238-7-37
Espresso – multi-valued PLA minimization, http://embedded.eecs.berkeley.edu/pubs/downloads/espresso
RSES – Rough Set Exploration System, http://logic.mimuw.edu.pl/~rses/
UC Irvine Machine Learning Repository, http://archive.ics.uci.edu/ml/
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Borowik, G., Kraśniewski, A., Łuba, T. (2015). Rule Induction Based on Logic Synthesis Methods. In: Selvaraj, H., Zydek, D., Chmaj, G. (eds) Progress in Systems Engineering. Advances in Intelligent Systems and Computing, vol 366. Springer, Cham. https://doi.org/10.1007/978-3-319-08422-0_118
Download citation
DOI: https://doi.org/10.1007/978-3-319-08422-0_118
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-08421-3
Online ISBN: 978-3-319-08422-0
eBook Packages: EngineeringEngineering (R0)