Advertisement

Comparison of Lazy Classification Algorithms Based on Deterministic and Inhibitory Decision Rules

  • Paweł Delimata
  • Mikhail Moshkov
  • Andrzej Skowron
  • Zbigniew Suraj
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5009)

Abstract

In the paper, two lazy classification algorithms of polynomial time complexity are considered. These algorithms are based on deterministic and inhibitory decision rules, but the direct generation of rules is not required. Instead of this, for any new object the considered algorithms extract from a given decision table efficiently some information about the set of rules. Next, this information is used by a decision-making procedure. The reported results of experiments show that the algorithms based on inhibitory decision rules are often better than those based on deterministic decision rules.

Keywords

Rough sets Decision tables Deterministic decision rules Inhibitory decision rules 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Aha, D.W. (ed.): Lazy Learning. Kluwer Academic Publishers, Dordrecht (1997)zbMATHGoogle Scholar
  2. 2.
    Bazan, J.G.: Discovery of Decision Rules by Matching New Objects Against Data Tables. In: Polkowski, L., Skowron, A. (eds.) RSCTC 1998. LNCS (LNAI), vol. 1424, pp. 521–528. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  3. 3.
    Bazan, J.G.: A Comparison of Dynamic and Non-Dynamic Rough Set Methods for Extracting Laws from Decision Table. In: Polkowski, L., Skowron, A. (eds.) Rough Sets in Knowledge Discovery, pp. 321–365. Physica-Verlag, Heidelberg (1998)Google Scholar
  4. 4.
    Bazan, J.G.: Methods of Approximate Reasoning for Synthesis of Decision Algorithms. Ph.D. Thesis. Warsaw University (in Polish) (1998)Google Scholar
  5. 5.
    Data Mining Exploration System (Software), http://www.univ.rzeszow.pl/rspn
  6. 6.
    Delimata, P., Moshkov, M., Skowron, A., Suraj, Z.: Two Families of Classification Algorithms. In: An, A., Stefanowski, J., Ramanna, S., Butz, C.J., Pedrycz, W., Wang, G. (eds.) RSFDGrC 2007. LNCS (LNAI), vol. 4482, pp. 297–304. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  7. 7.
    Moshkov, M., Skowron, A., Suraj, Z.: On Maximal Consistent Extensions of Information Systems. In: Conference Decision Support Systems (Zakopane, Poland, December 2006), University of Silesia, Katowice, vol. 1, pp. 199–206 (2007)Google Scholar
  8. 8.
    UCI Repository of Machine Learning Databases, University of California, Irvine http://www.ics.uci.edu/~mlearn/MLRepository.html
  9. 9.
    Nguyen, H.S.: Scalable Classification Method Based on Rough Sets. In: Alpigini, J.J., Peters, J.F., Skowron, A., Zhong, N. (eds.) RSCTC 2002. LNCS (LNAI), vol. 2475, pp. 433–440. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  10. 10.
    Nguyen, T.T.: Handwritten Digit Recognition Using Adaptive Classifier Construction Techniques. In: Pal, S.K., Polkowski, L., Skowron, A. (eds.) Rough-Neural Computing: Techniques for Computing with Words, pp. 573–585. Springer, Heidelberg (2003)Google Scholar
  11. 11.
    Pawlak, Z.: Rough Sets – Theoretical Aspects of Reasoning about Data. Kluwer Academic Publishers, Dordrecht (1991)zbMATHGoogle Scholar
  12. 12.
    Pawlak, Z., Skowron, A.: Rudiments of Rough Sets. Information Sciences 177, 3–27 (2007); Rough Sets: Some Extensions. Information Sciences 177, 28–40 (2007); Rough Sets and Boolean Reasoning. Information Sciences 177, 41–73 (2007)zbMATHCrossRefMathSciNetGoogle Scholar
  13. 13.
    Rough Set Exploration System, http://logic.mimuw.edu.pl/~rsesGoogle Scholar
  14. 14.
    Skowron, A., Suraj, Z.: Rough Sets and Concurrency. Bulletin of the Polish Academy of Sciences 41, 237–254 (1993)zbMATHGoogle Scholar
  15. 15.
    Suraj, Z.: Some Remarks on Extensions and Restrictions of Information Systems. In: Ziarko, W., Yao, Y. (eds.) RSCTC 2000. LNCS (LNAI), vol. 2005, pp. 204–211. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  16. 16.
    Wojna, A.: Analogy-Based Reasoning in Classifier Construction (Ph.D. Thesis). In: Peters, J.F., Skowron, A. (eds.) Transactions on Rough Sets IV. LNCS, vol. 3700, pp. 277–374. Springer, Heidelberg (2005)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Paweł Delimata
    • 1
  • Mikhail Moshkov
    • 2
  • Andrzej Skowron
    • 3
  • Zbigniew Suraj
    • 1
  1. 1.Chair of Computer ScienceUniversity of RzeszówRzeszówPoland
  2. 2.Institute of Computer ScienceUniversity of SilesiaSosnowiecPoland
  3. 3.Institute of MathematicsWarsaw UniversityWarsawPoland

Personalised recommendations