Variable precision extension of rough sets
Fundamenta Informaticae - Special issue: rough sets
An Investigation of beta-Reduct Selection within the Variable Precision Rough Sets Model
RSCTC '00 Revised Papers from the Second International Conference on Rough Sets and Current Trends in Computing
Decision Making with Probabilistic Decision Tables
RSFDGrC '99 Proceedings of the 7th International Workshop on New Directions in Rough Sets, Data Mining, and Granular-Soft Computing
Evaluation of probabilistic decision tables
RSFDGrC'03 Proceedings of the 9th international conference on Rough sets, fuzzy sets, data mining, and granular computing
RSFDGrC'03 Proceedings of the 9th international conference on Rough sets, fuzzy sets, data mining, and granular computing
An illustration of the effect of continuous valued discretisation in data analysis using VPRSβ
RSFDGrC'03 Proceedings of the 9th international conference on Rough sets, fuzzy sets, data mining, and granular computing
Determination of the threshold value β of variable precision rough set by fuzzy algorithms
International Journal of Approximate Reasoning
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The extended variable precision rough sets model incorporating asymmetric bounds is a generalisation of the original rough set theory. This paper introduces the (l, u)-quality graph, which elucidates the associated level of quality of classification (QoC), based on the choice of l and u values. A number of summary measures and lines are defined which pass over the domain of the (l, u)-quality graph. The defined lines are used to identify a choice of l and u values, based on retaining the underlying level of QoC from the whole (l, u)- quality graph.