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Tytuł artykułu

Deterministic and nondeterministic decision rules in classification process

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
In this paper an algorithm of calculating nondeterministic decision rules from the decision table was presented. The algorithm uses additional conditions imposed on rules. This is a greedy algorithm. The nondeterministic decision rules were used in the process of classification of new examples, for medical data sets. The decision tables from the UCI Machine Learning Repository were used. The achieved results allow us to state that nondeterministic decision rules can be used for improving the quality of classification.
Rocznik
Tom
Strony
87--92
Opis fizyczny
Bibliogr. 29 poz., tab.
Twórcy
autor
  • Institute of Computer Science, University of Silesia, Będzińska 39, 41-200 Sosnowiec, Poland
Bibliografia
  • [1] BUDIHARDJO A., GRZYMALA–BUSSE J., WOOLERY L., Program LERS_LB 2.5 as a tool for knowledge acquisition in nursing, In: Proceedings of the 4th Int. Conference on Industrial & Engineering Applications of AI & Expert Systems, 1991, pp. 735–74.
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  • [3] ASUNCION A., NEWMAN D.J., UCI Machine Learning Repository. University of California, Irvine, School of Information and Computer Sciences, 2007.
  • [4] BAZAN J.G., SZCZUKA M.S., WOJNA A., WOJNARSKI M., On the Evolution of Rough Set Exploration System. In: S. Tsumoto et. al. (eds.), RSCTC 2004, LNAI, Vol. 3066, Springer, Heidelberg, 2004, pp. 592–601.
  • [5] CARLIN U., KOMOROWSKI J., OHRN A., Rough set analysis of patients with suspected acute appendicitis. In: B. Bouchon–Meunier, R.R. Yager, (eds.), Proc. Seventh Conference on Information Processing and Management of Uncertainty in Knowledge–Based Systems, (IPMU'98). Paris, France, Edisions, 1998, pp. 1528–1533.
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  • [7] DELIMATA P., MOSHKOV M., SKOWRON A., SURAJ Z., Inhibitory Rules in Data Analysis: A Rough Set Approach. Studies in Computational Intelligence, Vol. 163, Springer, Heidelberg, 2009.
  • [8] DEMIROZ G., GOVENIR H. A., ILTER N., Learning Differential Diagnosis of Eryhemato–Squamous Diseases using Voting Feature Intervals, Aritificial Intelligence in Medicine, Vol. 13, 1998, pp. 147.
  • [9] GRZYMALA–BUSSE J.W., LERS – A Data Mining System. In: Maimon, O., Rokach, L. (eds.), The Data Mining and Knowledge Discovery Handbook. Springer, New York, 2005, pp. 1347–1351.
  • [10] GRZYMALA–BUSSE J.W., GOODWIN L.K., A comparison of less specific versus more specific rules for preterm birth prediction, Proceedings of the First Online Workshop on Soft Computing WSC1 on the Internet, Japan, 1996, pp. 129–133.
  • [11] JELONEK J., KRAWIEC K., SLOWINSKI R., STEFANOWSKI J., SZYMAS J., Neural networks rough sets – Comparison combination for classification of histological pictures, In: W. Ziarko (ed.), Rough Sets, Fuzzy Sets and Knowledge Discovery (RSKD'93), Springer–Verlag & British Computer Society, London, Berlin, 1994, pp. 426–433.
  • [12] KOMOROWSKI J., PAWLAK Z., POLKOWSKI L., SKOWRON A., Rough sets: A tutorial, in S.K. Pal and A. Skowron (eds.), Rough–Fuzzy Hybridization: A New Trend in Decision–Making, Springer–Verlag, Singapor, 1999, pp. 3–98.
  • [13] MARSZAŁ–PASZEK B., PASZEK P., Minimal Templates and Knowledge Discovery. In: M. Kryszkiewicz et al. (eds.), RSEISP 2007. LNAI, Vol. 4585, Springer, Heidelberg, 2007, pp. 411–416.
  • [14] 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.
  • [15] MOSHKOV M., SKOWRON A., SURAJ Z., Maximal consistent extensions of information systems relative to their theories. Information Sciences 178 (12), 2008, pp. 2600–2620.
  • [16] NAKAI K., KANEHISA M., Expert Sytem for Predicting Protein Localization Sites in Gram–Negative Bacteria, Proteins: Structure, Function, and Genetics 11, 1991, pp. 95–110.
  • [17] NGUYEN S.H., SKOWRON A., SYNAK P., WRÓBLEWSKI J., Knowledge Discovery in Databases: Rough Set Approach, Seventh International Fuzzy Systems Association World Congress (IFSA’1997), Vol. 2, Academia, Prague, 1997, pp. 204–209.
  • [18] PAWLAK Z., Rough Sets: Theoretical aspects of reasoning about data. Boston: Kluwer Academic Publishers, 1991.
  • [19] PAWLAK Z., SKOWRON A., Rudiments of Rough Sets. Information Sciences 177, 2007, pp. 3–27; Rough Sets: Some Extensions. Information Sciences 177, pp. 28–40; Rough Sets and Boolean Reasoning. Information Science 177, pp. 41–73.
  • [20] PULATOVA S., Covering (Rule–Based) Algorithms. In: Berry, M.W., Browne, M. (eds.), Lecture Notes in Data Mining. World Scientific, Singapore, 2006, pp. 87–97.
  • [21] Rosetta: http://www.lcb.uu.se/tools/rosetta/
  • [22] Rough Set Exploration System: http://logic.mimuw.edu.pl/~rses.
  • [23] SWINIARSKI R., Rough sets Bayesian methods applied to cancer detection, In: L. Polkowski, A. Skowron (eds.), Proc. First International Conference on Rough Sets and Soft Computing RSCTC'98. Warszawa, Poland, LNAI 1424, Springer–Verlag, 1998, pp. 609–616.
  • [24] SKOWRON A., SURAJ Z., Rough sets and concurrency, Bulletin of the Polish Academy of Sciences 41 No. 3, 1993, pp. 237–254.
  • [25] SURAJ Z., Some Remarks on Extensions and Restrictions of Information Systems, In: W. Ziarko, Y.Y. Yao (eds.), RSCTC 2000. LNCS, Vol. 2005. Springer, Heidelberg, 2001, pp. 204–211.
  • [26] 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.
  • [27] TSUMOTO S., Domain experts' interpretation of rules induced from clinical databases. In: H.J. Zimmermann (ed.), Proceedings of the Fifth European Congress on Intelligent Techniques and Soft Computing (EUFIT'97), Aachen, Germany, Verlag Mainz, 1997, pp. 1639–1642.
  • [28] WAKULICZ–DEJA A., PASZEK P., Diagnose Progressive Encephalopathy Applying the Rough Set Theory, International Journal of Medical Informatics, 46, 1997, pp. 119–127.
  • [29] 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.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-PWA4-0017-0015
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