Rough sets and intelligent data analysis
Information Sciences—Informatics and Computer Science: An International Journal
RSFDGrC '99 Proceedings of the 7th International Workshop on New Directions in Rough Sets, Data Mining, and Granular-Soft Computing
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
Indiscernibility Relation for Continuous Attributes: Application in Image Recognition
RSEISP '07 Proceedings of the international conference on Rough Sets and Intelligent Systems Paradigms
Dominance-based rough set approach as a proper way of handling graduality in rough set theory
Transactions on rough sets VII
Rough feature selection for intelligent classifiers
Transactions on rough sets VII
Reduct-based analysis of decision algorithms: application in computational stylistics
HAIS'11 Proceedings of the 6th international conference on Hybrid artificial intelligent systems - Volume Part II
Application of DRSA-ANN classifier in computational stylistics
ISMIS'11 Proceedings of the 19th international conference on Foundations of intelligent systems
Rule-Based approach to computational stylistics
SIIS'11 Proceedings of the 2011 international conference on Security and Intelligent Information Systems
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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When the indiscernibility relation, fundamental to Classical Rough Set Approach, is substituted with dominance relation, it results in Dominance-Based Rough Set Approach to data analysis. It enables support not only for nominal classification tasks, but also when ordinal properties on attribute values can be observed [1], making DRSA methodology well suited for stylometric processing of texts. Stylometry involves handling quantitative features of texts leading to characterisation of authors to the point of recognition of their individual writing styles. As always, selection of attributes is crucial to classification accuracy, as is the construction of a decision algorithm. When minimal cover gives unsatisfactory results, and all rules on examples algorithm returns very high number of rules, usually constraints are imposed by selection of some reduct and limiting the decision algorithm by including within it only rules with certain support. However, reducts are typically numerous and within them some of conditional attributes are used more often than others, which is also true for conditions specified by decision rules. The paper presents observations how the frequency of usage for features reflects on the performance of decision algorithms resulting from selection of rules with conditional attributes exploited most and least often.