DRSA Decision Algorithm Analysis in Stylometric Processing of Literary Texts
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 , 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.
KeywordsDRSA Decision Algorithm Relative Reduct Feature Selection Stylometry Data Mining
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