Nondeterministic decision rules in classification process for medical data

• Autorzy: Marszał-Paszek B. , Paszek P.
• Języki publikacji: EN

Abstrakty

EN

In the paper, we discuss nondeterministic rules in decision tables, called the second type nondeterministic rules. They have a few decisions values on the right hand side but on the left hand side only one attribute that has two values. We show that these kinds of rules can be used for improving the quality of classification. It is important in rule-based diagnosis support systems, where classification error can lead to serious consequences. The well known greedy strategy to construct the new nondeterministic rules, have been proposed. Additionally, based on deterministic and nondeterministic (second type) rules, classification algorithm with polynomial computational complexity has been developed. This rule-based classifier was tested on the group of decision tables, containing medical data, from the UCI Machine Learning Repository. The reported results of experiments showing that by combining rule-based classifier based on deterministic rules with second type nondeterministic rules give us possibility to improve the classification quality.

Słowa kluczowe

EN

nondeterministic decision rules; classification; decision tables; rough sets; rule-based classifiers

PL

niedeterministyczne reguły decyzyjne; klasyfikacja; tablice decyzyjne; zbiory przybliżone; zasada klasyfikatora

• Wydawca: University of Silesia, Institute of Informatics, Computer Systems Department
• Czasopismo: Journal of Medical Informatics & Technologies
• Rocznik: 2011
• Tom: Vol. 17
• Strony: 59--64
• Opis fizyczny: Bibliogr. 22 poz., tab.

Twórcy

autor

Marszał-Paszek B.

• Institute of Computer Science, University of Silesia, Będzińska 39, 41-200 Sosnowiec, Poland

autor

Paszek P.

Bibliografia

• [1] AGRAWAL R., IMIELINSKI T, SWAMI A., Mining Association Rules Between Sets of Items in Large Databases, Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data, Washington D.C., New York, ACM Press, 1993, pp. 207–216.
• [2] ASUNCION A., NEWMAN D.J., UCI Machine Learning Repository, University of California, Irvine School of Information and Computer Sciences, 2007.
• [3] BAZAN J.G., SZCZUKA M.S., WOJNA A., WOJNARSKI M., On the Evolution of Rough Set Exploration System, RSCTC 2004, LNAI, Vol. 3066, Springer, Heidelberg, 2004, pp. 592–601.
• [4] BUDIHARDJO A., GRZYMAŁA-BUSSE J., WOOLERY L., Program LERS_LB 2.5 as a tool for knowledge acquisition in nursing, Proceedings of the 4th Int. Conference on Industrial & Engineering Applications of AI & Expert Systems, 1991, pp. 735–740.
• [5] CARLIN U., KOMOROWSKI J., OHRN A., Rough set analysis of patients with suspected acute appendicitis, in: BOUCHON-MEUNIER G., YAGER R.R. (eds.), Proc. Seventh Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU'98), Paris, France, 1998, pp. 1528–1533.
• [6] CESTNIK G., KONENENKO I., BRATKO I., Assistant-86: A Knowledge-Elicitation Tool for Sophisticated Users, Progress in Machine Learning, Sigma Press, 1987, pp. 31–45.
• [7] DELIMATA P., MARSZAŁ-PASZEK B., MOSHKOV M., PASZEK P., SKOWRON A., SURAJ Z., Comparison of Some Classification Algorithms Based on Deterministic and Nondeterministic Decision Rules, Transactions on Rough Sets XII, LNCS 6190, Springer, Heidelberg, 2010, pp. 90–105.
• [8] DEMIROZ G., GOVENIR H.A., ILTER N., Learning Differential Diagnosis of Eryhemato-Squamous Diseases using Voting Feature Intervals, Artificial Intelligence in Medicine, Vol. 13, 1998, pp. 147–165.
• [9] GRZYMAŁA-BUSSE J.W., LERS – A Data Mining System. The Data Mining and Knowledge Discovery Handbook, Springer, New York, 2005, pp. 1347–1351.
• [10] MICHALSKI R., http://www.mli.gmu.edu/michalski/
• [11] MICHALSKI R., MOZETIC I., HONG J., LAVRAC N., The Multi-Purpose Incremental Learning System AQ15 and its Testing Applications to Three Medical Domains, Proceedings of the Fifth National Conference on Artificial Intelligence, Philadelphia, Morgan Kaufmann, 1986, pp. 1041–1045.
• [12] NAKAI K., KANEHISA M., Expert System for Predicting Protein Localization Sites in Gram-Negative Bacteria, Proteins: Structure, Function, and Genetics, Vol. 11, 1991, pp. 95–110.
• [13] PASZEK P., MARSZAŁ-PASZEK B., Deterministic and Nondeterministic Decision Rules in Classification Process, Journal of Medical Informatics and Technologies, Vol. 15, 2010, pp. 87–92.
• [14] PAWLAK Z., Rough Sets: Theoretical aspects of reasoning about data, Boston: Kluwer Academic Publishers, 1991.
• [15] PAWLAK Z., SKOWRON A., Rudiments of Rough Sets, Information Sciences 177, pp. 3–27; Rough Sets: Some Extensions, Information Sciences 177, pp. 28–40; Rough Sets and Boolean Reasoning, Information Sciences, Vol. 177, 2007, pp. 41–73.
• [16] RISSANEN J., Modelling by Shortest Data Description, Automatica 14, 1978, pp. 465–471.
• [17] Rosetta: http://www.lcb.uu.se/tools/rosetta/.
• [18] Rough Set Exploration System: http://logic.mimuw.edu.pl/rses.
• [19] SWINIARSKI R., Rough sets Bayesian methods applied to cancer detection, RSCTC'98, LNAI, Vol. 1424, Springer-Verlag, 1998, pp. 609–616.
• [20] TRIANTAPHYLLOU E., FELICI G., (eds.), Data Mining and Knowledge Discovery Approaches Based on Rule Induction Techniques, Springer Science and Business Media, LLC, New York, 2006.
• [21] TSUMOTO S., Modelling Medical Diagnostic Rules Based on Rough Sets, RSCTC 1998, LNCS, Vol. 1424, Springer-Verlag, Berlin, 1998, pp. 475–482.
• [22] WOOLERY K., GRZYMAŁA–BUSSE J., Machine learning for an Expert System to Predict Preterm Birth Risk, J. Am. Med. Informatics Assoc. 1, 1994, pp. 439–446.