Rule-Based Approach to Computational Stylistics

  • Urszula Stańczyk
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7053)


Decision algorithms correspond to the rule-based approach to classification and pattern recognition problems. While to shorten the processing time we need as few constituent decision rules as possible, when their number is too low it may lead to a poor performance of the classifier. The decision rules can be found by providing the minimal cover of the training samples, by calculating rules with some genetic algorithms, by the exhaustive search for all rules. This last option offers the widest choice of rules, which enables tailoring the final algorithm to the task at hand, yet this is achieved by the additional cost of rule selection process. Usually there are assumed some measures indicating the quality of individual decision rules. The paper presents a different procedure, which is closer to feature reduction. In the first step there are selected condition attributes that are discarded, then the rules that contain conditions on these attributes are removed from the algorithm. The classifier performance is observed in the domain of computational stylistics, which is a study on characteristics of writing styles.


Decision Algorithm Computational Stylistics Rough Sets DRSA Condition Attribute Rule Support 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Baszczynski, J., Sowinski, R., Szelaga, M.: Sequential covering rule induction algorithm for variable consistency rough set approaches. Information Sciences 181(5), 987–1002 (2011)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Burrows, J.: Textual analysis. In: Schreibman, S., Siemens, R., Unsworth, J. (eds.) A Companion to Digital Humanities. Blackwell, Oxford (2004)Google Scholar
  3. 3.
    Craig, H.: Stylistic analysis and authorship studies. In: Schreibman, S., Siemens, R., Unsworth, J. (eds.) A Companion to Digital Humanities. Blackwell, Oxford (2004)Google Scholar
  4. 4.
    Greco, S., Matarazzo, B., Słowiński, R.: Handling missing values in rough set analysis of multi-attribute and multi-criteria decision problems. In: Zhong, N., Skowron, A., Ohsuga, S. (eds.) RSFDGrC 1999. LNCS (LNAI), vol. 1711, pp. 146–157. Springer, Heidelberg (1999)CrossRefGoogle Scholar
  5. 5.
    Greco, S., Matarazzo, B., Slowinski, R.: The use of rough sets and fuzzy sets in Multi Criteria Decision Making. In: Gal, T., Hanne, T., Stewart, T. (eds.) Advances in Multiple Criteria Decision Making, pp. 14.1–14.59. Kluwer Academic Publishers, Dordrecht Boston (1999)Google Scholar
  6. 6.
    Hu, X., Han, J., Lin, T.Y.: A new rough sets model based on database systems. Fundamenta Informaticae 20, 1–18 (2004)MathSciNetzbMATHGoogle Scholar
  7. 7.
    Li, J., Cercone, N.: Introducing a rule importance measure. Transactions on Rough Sets 5, 167–189 (2006)zbMATHGoogle Scholar
  8. 8.
    Moshkov, M., Piliszczuk, M., Zielosko, B.: On partial covers, reducts and decision rules with weights. Transactions on Rough Sets 6, 211–246 (2006)zbMATHGoogle Scholar
  9. 9.
    Pawlak, Z.: Rough sets and intelligent data analysis. Information Sciences 147, 1–12 (2002)MathSciNetCrossRefzbMATHGoogle Scholar
  10. 10.
    Peng, R., Hengartner, H.: Quantitative analysis of literary styles. The American Statistician 56(3), 15–38 (2002)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Słowiński, R., Greco, S., Matarazzo, B.: Dominance-based rough set approach to reasoning about ordinal data. In: Kryszkiewicz, M., Peters, J.F., Rybiński, H., Skowron, A. (eds.) RSEISP 2007. LNCS (LNAI), vol. 4585, pp. 5–11. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  12. 12.
    Stańczyk, U.: Dominance-based rough set approach employed in search of authorial invariants. In: Kurzyński, M., Woźniak, M. (eds.) Computer Recognition Systems 3. AISC, vol. 57, pp. 315–323. Springer, Berlin (2009)Google Scholar
  13. 13.
    Stańczyk, U.: DRSA decision algorithm analysis in stylometric processing of literary texts. In: Szczuka, M., Kryszkiewicz, M., Ramanna, S., Jensen, R., Hu, Q. (eds.) RSCTC 2010. LNCS (LNAI), vol. 6086, pp. 600–609. Springer, Heidelberg (2010)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Urszula Stańczyk
    • 1
  1. 1.Institute of InformaticsSilesian University of TechnologyGliwicePoland

Personalised recommendations