2010
DOI: 10.1007/978-3-642-11479-3_8
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Abstract: Abstract. The internet service NetTRS (Network TRS) that enable to realize induction, evaluation, and postprocessing of decision rules is presented in the paper. The TRS (Tolerance Rough Sets) library is the main part of the service. The TRS library makes possible to induct, generalize and filtrate decision rules. Moreover, TRS enables to evaluate rules and conduct the classification process. The NetTRS service is a package of the library in user interface and makes it accessible in the Internet. NetTRS put pr… Show more

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Cited by 27 publications
(36 citation statements)
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References 36 publications
(36 reference statements)
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“…The quality measure applied in decision rule induction is significant for the quality of an output set of rules. This is confirmed by empirical research [32,20,21,23,25,26].…”
Section: Related Worksupporting
confidence: 60%
See 1 more Smart Citation
“…The quality measure applied in decision rule induction is significant for the quality of an output set of rules. This is confirmed by empirical research [32,20,21,23,25,26].…”
Section: Related Worksupporting
confidence: 60%
“…If the analysis objective is to create a classification system that uses an interpretable data model, application of sequential covering rule induction algorithms is the most sensible solution. The quality of the rule set obtained by the covering algorithm depends on the quality measure [20,1,21,22,23,24,25,26] used in the growing and pruning phases. The used quality measure is one of the factors affecting the classification accuracy, the number of rules induced and their other characteristics (e.g.…”
Section: Introductionmentioning
confidence: 99%
“…Wisconsin Breast Cancer data set is not such popular, but our results can be compared to approaches (92-99%) presented and referenced in [10] for different rule set sizes. Although the efficiency results for the Heart Disease and Appendicitis data sets are not enough satisfactory, they are better or competitive to approaches presented and referenced in [19,25].…”
Section: Discussionmentioning
confidence: 85%
“…RSES V2 presents the rules in the following format: RSES V2 p rovides the consumer with an option to shorten the generated rules, in addition to the basic functionalities presented in the theory of Rough Sets. Rule shortening and generalization has been investigated by researchers [43][44][45]. In RSES, ru le shortening is an advanced process that tries to condense the descriptor provided along with the premise of the rules [46].…”
Section: A Rough Sets Toolmentioning
confidence: 99%