On Preference Order of DRSA Conditional Attributes for Computational Stylistics

  • Urszula Stańczyk
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8056)


Computational stylistics (or stylometry) advocates that any writing style can be uniquely defined by quantitative measures. The patterns observed in textual descriptors are specific to authors in such high degree that it enables their recognition. In processing there can be employed techniques from artificial intelligence domain such as Dominance-Based Rough Set Approach (DRSA). Its starting point comprises establishing a preference order in value sets of attributes, which can be dictated by domain knowledge, arbitrarily assumed, or obtained through some analysis. The paper describes the process of finding preference orders through such sequential adjustments that lead to induction of minimal cover decision algorithms with the highest classification accuracy.


Computational Stylistics Authorship Attribution DRSA Decision Algorithm Preference Order Conditional Attributes 


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  1. 1.
    Abraham, A., Falcón, R., Bello, R. (eds.): Rough Set Theory: A True Landmark in Data Analysis. SCI, vol. 174. Springer, Berlin (2009)Google Scholar
  2. 2.
    Argamon, S., Burns, K., Dubnov, S. (eds.): The structure of style: Algorithmic approaches to understanding manner and meaning. Springer, Berlin (2010)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., Slowinski, R.: Rough set theory for multicriteria decision analysis. European Journal of Operational Research 129(1), 1–47 (2001)MathSciNetzbMATHCrossRefGoogle Scholar
  5. 5.
    Pawlak, Z.: Rough sets and intelligent data analysis. Information Sciences 147, 1–12 (2002)MathSciNetzbMATHCrossRefGoogle Scholar
  6. 6.
    Peng, R., Hengartner, H.: Quantitative analysis of literary styles. The American Statistician 56(3), 15–38 (2002)MathSciNetCrossRefGoogle Scholar
  7. 7.
    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
  8. 8.
    Stańczyk, U.: Decision rule length as a basis for evaluation of attribute relevance. Journal of Intelligent and Fuzzy Systems 24(3), 429–445 (2013)Google Scholar
  9. 9.
    Stańczyk, U.: Rough set and artificial neural network approach to computational stylistics. In: Ramanna, S., Jain, L., Howlett, R. (eds.) Emerging Paradigms in Machine Learning. SIST, vol. 13, pp. 441–470. Springer, Heidelberg (2013)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

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

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