Abstract
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.
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References
Abraham, A., Falcón, R., Bello, R. (eds.): Rough Set Theory: A True Landmark in Data Analysis. SCI, vol. 174. Springer, Berlin (2009)
Argamon, S., Burns, K., Dubnov, S. (eds.): The structure of style: Algorithmic approaches to understanding manner and meaning. Springer, Berlin (2010)
Craig, H.: Stylistic analysis and authorship studies. In: Schreibman, S., Siemens, R., Unsworth, J. (eds.) A Companion to Digital Humanities. Blackwell, Oxford (2004)
Greco, S., Matarazzo, B., Slowinski, R.: Rough set theory for multicriteria decision analysis. European Journal of Operational Research 129(1), 1–47 (2001)
Pawlak, Z.: Rough sets and intelligent data analysis. Information Sciences 147, 1–12 (2002)
Peng, R., Hengartner, H.: Quantitative analysis of literary styles. The American Statistician 56(3), 15–38 (2002)
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)
Stańczyk, U.: Decision rule length as a basis for evaluation of attribute relevance. Journal of Intelligent and Fuzzy Systems 24(3), 429–445 (2013)
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)
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Stańczyk, U. (2013). On Preference Order of DRSA Conditional Attributes for Computational Stylistics. In: Decker, H., Lhotská, L., Link, S., Basl, J., Tjoa, A.M. (eds) Database and Expert Systems Applications. DEXA 2013. Lecture Notes in Computer Science, vol 8056. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40173-2_4
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DOI: https://doi.org/10.1007/978-3-642-40173-2_4
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