Rough sets and intelligent data analysis
Information Sciences—Informatics and Computer Science: An International Journal
Dominance-Based Rough Set Approach to Reasoning About Ordinal Data
RSEISP '07 Proceedings of the international conference on Rough Sets and Intelligent Systems Paradigms
On Covering Attribute Sets by Reducts
RSEISP '07 Proceedings of the international conference on Rough Sets and Intelligent Systems Paradigms
DRSA decision algorithm analysis in stylometric processing of literary texts
RSCTC'10 Proceedings of the 7th international conference on Rough sets and current trends in computing
Rough set-based analysis of characteristic features for ANN classifier
HAIS'10 Proceedings of the 5th international conference on Hybrid Artificial Intelligence Systems - Volume Part I
Decision rule length as a basis for evaluation of attribute relevance
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology - Recent Advances in Soft Computing: Theories and Applications
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Computational stylistics or stylometry deals with characteristics of writing styles. It assumes that each author expresses themselves in such an individual way that a writing style can be uniquely defined and described by some quantifiable measures. With help of contemporary computers the stylometric tasks of author characterisation, comparison, and attribution can be implemented using either some statistic-oriented approaches or methodologies from artificial intelligence domain. The paper presents results of research on an application of a hybrid classifier, combining Dominance-based Rough Set Approach and Artificial Neural Networks, within the task of authorship attribution for literary texts. The performance of the classifier is observed while exploiting an analysis of characteristic features basing on the cardinalities of relative reducts found within rough set processing.