Abstract
In this paper we present an exemplary algorithm classifying new objects by matching them directly against data table to generate relevant decision instead of matching it against all rules generated from data table (see [1]). We report results of experiments on three medical data sets, concerning lymphography, breast cancer and primary tumor (see [8]).We compare standard methods for extracting laws from decision tables (see e.g. [17], [1]), based on rough set (see [13]) and boolean reasoning (see [2]), with the method based on algorithms calculating relevant decision rules for new objects. We also compare the results of computer experiments on those data sets obtained by applying our system based on rough set methods with the results on the same data sets obtained with help of several data analysis systems known from literature.
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Bazan, J.: A Comparison of Dynamic and non-Dynamic Rough Set Methods for Extracting Laws from Decision Table. To appear in Rough Sets in Knowledge Discovery, L. Polkowski and A. Skowron (eds.), Physica Verlag, 46 pages.
Brown, E., M.: Boolean reasoning. Kluwer Academic Publishers, Dordrecht (1990)
Cestnik, B., Kononenko, I., Bratko, I.: ASSISTANT 86: A knowledge elicitation tool for sophisticated users. In: Proceedings of EWSL-87, Bled, Yugoslavia (1987) 31–47
Clark, P., Niblett, T.: The CN2 induction algorithm. Machine Learning 3 (1989) 261–284
Dzeroski, S.: Handling noise in inductive logic programming. MSc Thesis, Dept. of EE and CS, University of Ljubljana, Slovenia (1991)
Grzymała-Busse, J. W.: A new version of the rule induction system LERS. Fundamenta Informaticae 31 (1997) 27–39
Michalski, R., Carbonell, J., G. and Mitchel, T., M. (eds.): Machine learning 1, Tioga/Morgan Kaufmann, Los Altos, CA (1983)
Michalski, R.,S., Mozetic, I., Hong, J. and Lavrac, N.: The multi-purpose incremental learning system AQ15 and its testing to three medical domains. In: Proceedings of AAAI-86, Morgan Kaufmann, San Mateo, CA (1986) 1041–1045
Michie, D., Spiegelhalter, D., J., Taylor, C., C.: Machine learning, neural and statistical classification. Ellis Horwood, New York (1994)
Mollestad, T.: A rough set approach to default rules data mining. PhD Thesis, supervisor J. Komorowski, Norvegian Institute of Technology, Trondheim, Norway (1996)
Nguyen, S. Hoa, Nguyen, H. Son: Some efficient algorithms for rough set methods. In: Proceedings of the Sixth International Conference, Information Procesing and Management of Uncertainty in Knowledge-Based Systems (IPMU’96), July 1–5, Granada, Spain (1996) 2 1451–1456
Øhrn, A., Komorowski, J.: Rosetta — A rough set toolkit for analysis of data. In: Proceedings of Third International Joint Conference on Information Sciences (JCIS’97), Durham, NC, USA, March 1–5, 3 (1997) 403–407
Pawlak, Z.: Rough sets: Theoretical aspects of reasoning about data. Kluwer Academic Publishers, Dordrecht (1991)
Piasta, Z., Lenarcik, A., Tsumoto S.: Machine discovery in databases with probabilistic rough classifiers. In: S. Tsumoto, S. Kobayashi, T. Yokomori, H. Tanaka, and A. Nakamura (eds.): Proceedings of the Fourth International Workshop on Rough Sets, Fuzzy Sets, and Machine Discovery (RSFD’96), The University of Tokyo, November 6–8 (1996) 353–359
Quinlan, J., R.: C4.5: Programs for machine learning. Morgan Kaufmann, San Mateo, California (1993).
Skowron, A., Rauszer, C.: The discernibility matrices and functions in information systems. In: R. Słowiński (ed.): Intelligent Decision Support — Handbook of Applications and Advances of the Rough Sets Theory, Kluwer Academic Publishers, Dordrecht (1992) 331–362
Skowron, A.: Boolean reasoning for decision rules generation. In: J. Komorowski, Z.W. Ras (eds.), Proceedings of of the Seventh International Symposium on Methodologies for Intelligent Systems (ISMIS’93), Trondheim, Norway, June 15–18, 1993, Lecture Notes in Computer Science 689 (1993) 295–305
Słowiński, R., Stefanowski, J.: RoughDAS and roughClass’ software implementations of the rough sets approach. In: R. Słowiński (ed.): Intelligent Decision Support — Handbook of Applications and Advances of the Rough Sets Theory, Kluwer Academic Publishers, Dordrecht (1992) 445–456
Wróblewski, J.: Finding minimal reducts using genetic algorithm (extended version). In: P.P. Wang (ed.), Second Annual Joint Conference on Information Sciences (JCIS’95), September 28–October 1, Wrightsville Beach, North Carolina, USA (1995) 186–189
Ziarko, W.: Variable precision rough set model. Journal of Computer and System Sciences 40 (1993) 39–59
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 1998 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Bazan, J.G. (1998). Discovery of Decision Rules by Matching New Objects Against Data Tables. In: Polkowski, L., Skowron, A. (eds) Rough Sets and Current Trends in Computing. RSCTC 1998. Lecture Notes in Computer Science(), vol 1424. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-69115-4_72
Download citation
DOI: https://doi.org/10.1007/3-540-69115-4_72
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-64655-6
Online ISBN: 978-3-540-69115-0
eBook Packages: Springer Book Archive