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We investigate advantages of the decision bireducts and the approximate decision reducts within a rough-set-inspired framework for deriving attribute subset ensembles from data, wherein each of attribute ... We show relationships between the decision bireducts and some formulations of approximate decision reducts summarized in  . ... Such an ability to control the ensembles of bireducts with respect to the areas of objects that they cover is especially important for robustness of the resulting classification systems and completeness ...dblp:conf/fedcsis/StawickiW12 fatcat:5xdbv6zx7vhrlctvxnwt2vmfna
Lecture Notes in Computer Science
On the other hand, the bounded computation cost and robustness with regard to the number of attributes (the sizes of higher-level feature sets can be limited by applying simple filters on rule induction ... In the second series of experiments, the classification performance achieved with a combination of DRBS and the simple classification rule from Definition 3.1 was compared to the results of the Random ... Depending on applications, each of those properties can be useful, thus, a robust classification model should be able to achieve a high score regardless of the quality measure used for the assessment. ...doi:10.1007/978-3-642-54756-0_7 fatcat:yhgu7zhirjbdvgdbubbdo7hckq