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Learning First-Order Rules: A Rough Set Approach

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Warianty tytułu
Konferencja
International Conference on Soft Computing and Distributed Processing (SCDP'2002) (June 2002, Rzeszów, Poland).
Języki publikacji
EN
Abstrakty
EN
The aim of this paper is to introduce and investigate an algorithm RSRL for finding first-order logic rules. Rough set methodology is used in the process of selecting literals which may be a part of a rule. The criterion of selecting a literal is as follows: only such a literal is selected, which added to the rule makes the rule discerning the most examples which were indiscernible so far.
Słowa kluczowe
Wydawca
Rocznik
Strony
139--157
Opis fizyczny
Bibliogr. 17 poz., tab., wykr.
Twórcy
autor
  • Department of Computer Science, Białystok University of Technology, Wiejska 45A, 15-351 Bialystok, Poland
autor
  • Department of Computer Science, Białystok University of Technology, Wiejska 45A, 15-351 Bialystok, Poland
Bibliografia
  • [1] Dzeroski, S., Lavrac, N. (Eds.): Relational Data Mining, Springer-Verlag, Berlin, 2001.
  • [2] Document Understanding Database: ftp://ftp.mlnet.org/ml-archive/general/data/doc-understanding/.
  • [3] Esposito, R, Malerba, D., Semerano, G., Pazzani, M.: A Machine Learning Approach To Document Understanding, R. S. Michalski, G. Tecuci, (Eds.), Proceedings of the Second International Workshop on Multistrategy Learning , 1993, pp. 276-292.
  • [4] Liu, C, Zhong, N.: Rough Problem Settings for ILP Dealing with Imperfect Data, Computational Intelligence, 17(3), 2001, pp. 446-459.
  • [5] Midelfart, H., Komorowski, J.: A Rough Set Approach to Inductive Logic Programming, W. Ziarko, Y. Yao, (Eds.), Proceedings of the 2nd International Conference on Rough Sets and Current Trends in Computing (RSCTC-2000), 2000, pp. 158-166.
  • [6] Mitchell, T: Machine Learning, McGraw Hill, 1997.
  • [7] Pal, S.K., Skowron A. (Eds.): Rough Fuzzy Hybridization: A New Trend in Decision-Making, Springer-Verlag, Singapore, 1999.
  • [8] Pawlak, Z.: Rough Sets. Theoretical Aspects of Reasoning about Data, Kluwer Academic Publishers, Dordrecht, 1991.
  • [9] Polkowski, L., Skowron, A. (Eds.): Rough Sets in Knowledge Discovery 1 and 2. Physica-Verlag, Heidelberg, 1998.
  • [10] Siromoney, A., Inoue, K.: The generic Rough Set Inductive Logic Programming (gRS-ILP) model. T.Y. Lin, Y.Y. Yao, L.A. Zadeh (Eds.), Data Mining, Rough Sets and Granular Computing, Studies and Fuzziness and Soft Computing vol 95, Physica-Verlag, 2002, pp. 499-517.
  • [11] Skowron, A., Stepaniuk, J.: Tolerance Approximation Spaces, Fundamenta Informaticae, 27,1996, pp. 245-253.
  • [12] Stepaniuk, J.: Knowledge Discovery by Application of Rough Set Models, L. Polkowski, S. Tsumoto, T.Y. Lin, (Eds.) Rough Set Methods and Applications. New Developments in Knowledge Discovery in Information Systems, Physica-Verlag, Heidelberg, 2000, pp. 137-233.
  • [13] Stepaniuk, J., Goralczuk, L.: An Algorithm Generating First Order Rules Based on Rough Set Methods, (ed.) J. Stepaniuk, Zeszyty Naukowe Politechniki Białostockiej Informatyka nr 1, 2002, pp. 235-250. [in Polish]
  • [14] Stepaniuk, J., Maj, M.: Data Transformation and Rough Sets, Lecture Notes in Computer Science 1510, Springer-Verlag, 1998, pp. 441-449.
  • [15] System FOIL: http://www.cse.unsw.edu.au/~quinlan/.
  • [16] System LINUS: http://www.gmd.de/ml-archive/ILP/public/software/linus.
  • [17] System PROGOL: http://www-users.cs.york.ac.uk/~stephen/progol.html
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BUS2-0005-0058
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