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Random Probes in Computation and Assessment of Approximate Reducts

  • Andrzej Janusz
  • Dominik Ślęzak
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8537)

Abstract

We discuss applications of random probes in a process of computation and assessment of approximate reducts. By random probes we mean artificial attributes, generated independently from a decision vector but having similar value distributions to the attributes in the original data. We introduce a concept of a randomized reduct which is a reduct constructed solely from random probes and we show how to use it for unsupervised evaluation of attribute sets. We also propose a modification of the greedy heuristic for a computation of approximate reducts, which reduces a chance of including irrelevant attributes into a reduct. To support our claims we present results of experiments on high dimensional data. Analysis of obtained results confirms usefulness of random probes in a search for informative attribute sets.

Keywords

attribute selection attribute reduction high dimensional data approximate reducts 

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References

  1. 1.
    Modrzejewski, M.: Feature Selection Using Rough Sets Theory. In: Brazdil, P.B. (ed.) ECML 1993. LNCS, vol. 667, pp. 213–226. Springer, Heidelberg (1993)CrossRefGoogle Scholar
  2. 2.
    Pawlak, Z.: Rough Sets: Present State and the Future. Foundations of Computing and Decision Sciences 18(3-4), 157–166 (1993)MathSciNetzbMATHGoogle Scholar
  3. 3.
    Błaszczyński, J., Słowiński, R., Susmaga, R.: Rule-Based Estimation of Attribute Relevance. In: Yao, J., Ramanna, S., Wang, G., Suraj, Z. (eds.) RSKT 2011. LNCS, vol. 6954, pp. 36–44. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  4. 4.
    Jensen, R., Shen, Q.: New Approaches to Fuzzy-Rough Feature Selection. IEEE Transactions on Fuzzy Systems 17(4), 824–838 (2009)CrossRefGoogle Scholar
  5. 5.
    Janusz, A., Ślęzak, D.: Rough Set Methods for Attribute Clustering and Selection. Applied Artificial Intelligence 28(3), 220–242 (2014)CrossRefGoogle Scholar
  6. 6.
    Świniarski, R.W., Skowron, A.: Rough Set Methods in Feature Selection and Recognition. Pattern Recognition Letters 24(6), 833–849 (2003)CrossRefGoogle Scholar
  7. 7.
    Kohavi, R., John, G.H.: Wrappers for Feature Subset Selection. Artificial Intelligence 97, 273–324 (1997)CrossRefGoogle Scholar
  8. 8.
    Ślęzak, D.: Approximate Reducts in Decision Tables. In: Proceedings of IPMU 1996, vol. 3, pp. 1159–1164 (1996)Google Scholar
  9. 9.
    Abeel, T., Helleputte, T., de Peer, Y.V., Dupont, P., Saeys, Y.: Robust Biomarker Identification for Cancer Diagnosis with Ensemble Feature Selection Methods. Bioinformatics 26(3), 392–398 (2010)CrossRefGoogle Scholar
  10. 10.
    Guyon, I., Gunn, S., Nikravesh, M., Zadeh, L. (eds.): Feature Extraction, Foundations and Applications. STUDFUZZ, vol. 207. Physica-Verlag, Springer (2006)Google Scholar
  11. 11.
    Janusz, A., Stawicki, S.: Applications of Approximate Reducts to the Feature Selection Problem. In: Yao, J., Ramanna, S., Wang, G., Suraj, Z. (eds.) RSKT 2011. LNCS, vol. 6954, pp. 45–50. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  12. 12.
    Nguyen, H.S.: Approximate Boolean Reasoning: Foundations and Applications in Data Mining. In: Peters, J.F., Skowron, A. (eds.) Transactions on Rough Sets V. LNCS, vol. 4100, pp. 334–506. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  13. 13.
    Skowron, A., Rauszer, C.: The Discernibility Matrices and Functions in Information Systems. In: Słowiński, R. (ed.) Intelligent Decision Support. Theory and Decision Library, vol. 11, pp. 331–362. Kluwer (1992)Google Scholar
  14. 14.
    Bazan, J.G., Skowron, A., Synak, P.: Dynamic Reducts as a Tool for Extracting Laws from Decisions Tables. In: Raś, Z.W., Zemankova, M. (eds.) ISMIS 1994. LNCS, vol. 869, pp. 346–355. Springer, Heidelberg (1994)CrossRefGoogle Scholar
  15. 15.
    Ślęzak, D., Janusz, A.: Ensembles of Bireducts: Towards Robust Classification and Simple Representation. In: Kim, T.-h., Adeli, H., Slezak, D., Sandnes, F.E., Song, X., Chung, K.-I., Arnett, K.P. (eds.) FGIT 2011. LNCS, vol. 7105, pp. 64–77. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  16. 16.
    Ślęzak, D.: Various Approaches to Reasoning with Frequency-Based Decision Reducts: A Survey. In: Polkowski, L., Lin, T., Tsumoto, S. (eds.) Rough Sets in Soft Computing and Knowledge Discovery: New Developments, pp. 235–285. Physica-Verlag (2000)Google Scholar
  17. 17.
    Widz, S., Ślęzak, D.: Approximation Degrees in Decision Reduct-Based MRI Segmentation. In: Proceedings of FBIT 2007, pp. 431–436 (2007)Google Scholar
  18. 18.
    R Development Core Team: R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria (2008)Google Scholar
  19. 19.
    Soman, K.P., Diwakar, S., Ajay, V.: Insight into Data Mining: Theory and Practice. Prentice-Hall (2006)Google Scholar
  20. 20.
    Janusz, A., Nguyen, H.S., Ślęzak, D., Stawicki, S., Krasuski, A.: JRS’2012 Data Mining Competition: Topical Classification of Biomedical Research Papers. In: Yao, J., Yang, Y., Słowiński, R., Greco, S., Li, H., Mitra, S., Polkowski, L. (eds.) RSCTC 2012. LNCS (LNAI), vol. 7413, pp. 422–431. Springer, Heidelberg (2012)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Andrzej Janusz
    • 1
  • Dominik Ślęzak
    • 1
    • 2
  1. 1.Institute of MathematicsUniversity of WarsawWarsawPoland
  2. 2.Infobright Inc., PolandWarsawPoland

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