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Attribute Subset Quality Functions over a Universe of Weighted Objects

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

Abstract

We consider a rough set inspired approach to deriving meaningful attribute subsets from data organized in a form of a decision system. We focus on quality functions measuring degrees in which particular attribute subsets determine the values of a decision attribute. We follow a well known idea of assigning weights to the training objects in order to reflect their importance in the attribute subset selection and new case classification processes. We discuss an example of an object weighting strategy related to probabilities of decision classes in the training data. We show that two attribute subset quality functions used in our earlier research are the same function computed using two different weighting techniques. We also investigate whether it is worth using the same weights during the processes of attribute selection and new case classification.

Keywords

Approximate decision reducts Attribute subset quality functions Strategies of weighting objects Voting among decision rules 

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References

  1. 1.
    Świniarski, R.W., Skowron, A.: Rough Set Methods in Feature Selection and Recognition. Pattern Recognition Letters 24(6), 833–849 (2003)CrossRefGoogle Scholar
  2. 2.
    Bazan, J., Szczuka, M.S.: The Rough Set Exploration System. In: Peters, J.F., Skowron, A. (eds.) Transactions on Rough Sets III. LNCS, vol. 3400, pp. 37–56. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  3. 3.
    Ślęzak, D.: Rough Sets and Functional Dependencies in Data: Foundations of Association Reducts. In: Gavrilova, M.L., Tan, C.J.K., Wang, Y., Chan, K.C.C. (eds.) Transactions on Computational Science V. LNCS, vol. 5540, pp. 182–205. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  4. 4.
    Widz, S., Ślęzak, D.: Rough Set Based Decision Support – Models Easy to Interpret. In: Peters, G., Lingras, P., Ślęzak, D., Yao, Y. (eds.) Rough Sets: Selected Methods and Applications in Management & Engineering. Advanced Information and Knowledge Processing, pp. 95–112. Springer (2012)Google Scholar
  5. 5.
    Kuncheva, L.I., Diez, J.J.R., Plumpton, C.O., Linden, D.E.J., Johnston, S.J.: Random Subspace Ensembles for fMRI Classification. IEEE Transactions on Medical Imaging 29(2), 531–542 (2010)CrossRefGoogle Scholar
  6. 6.
    Ślęzak, D.: Normalized Decision Functions and Measures for Inconsistent Decision Tables Analysis. Fundamenta Informaticae 44(3), 291–319 (2000)MathSciNetzbMATHGoogle Scholar
  7. 7.
    Stawicki, S., Widz, S.: Decision Bireducts and Approximate Decision Reducts: Comparison of Two Approaches to Attribute Subset Ensemble Construction. In: Ganzha, M., Maciaszek, L.A., Paprzycki, M. (eds.) Federated Conference on Computer Science and Information Systems – FedCSIS 2012, Wrocław, Poland, September 9-12, pp. 331–338. IEEE (2012)Google Scholar
  8. 8.
    Pawlak, Z., Skowron, A.: Rudiments of Rough Sets. Information Sciences 177(1), 3–27 (2007)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Skurichina, M., Duin, R.P.W.: Bagging, Boosting and the Random Subspace Method for Linear Classifiers. Pattern Analysis and Applications 5(2), 121–135 (2002)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Ślęzak, D., Ziarko, W.: The Investigation of the Bayesian Rough Set Model. International Journal of Approximate Reasoning 40(1-2), 81–91 (2005)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Frank, A., Asuncion, A.: UCI Machine Learning Repository (2010), http://archive.ics.uci.edu/ml
  12. 12.
    Brodersen, K.H., Ong, C.S., Stephan, K.E., Buhmann, J.M.: The Balanced Accuracy and Its Posterior Distribution. In: 20th International Conference on Pattern Recognition, ICPR 2010, Istanbul, Turkey, August 23-26, pp. 3121–3124. IEEE (2010)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Sebastian Widz
    • 1
  • Dominik Ślęzak
    • 2
    • 3
  1. 1.Systems Research InstitutePolish Academy of SciencesWarsawPoland
  2. 2.Institute of MathematicsUniversity of WarsawWarsawPoland
  3. 3.Infobright Inc., PolandWarsawPoland

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