A weighted rough set based method developed for class imbalance learning
Information Sciences: an International Journal
A Weighted Rough Set Approach for Cost-Sensitive Learning
RSFDGrC '07 Proceedings of the 11th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing
Weighted rough set learning: towards a subjective approach
PAKDD'07 Proceedings of the 11th Pacific-Asia conference on Advances in knowledge discovery and data mining
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Equal set is the most important concept in rough set. In classic rough set model, the equality is strong, must be very precise. But strong equality cause inapplicable due to data noise. Variable precision rough set model solve the data noise by introducing an error-tolerated factor. But there are no weighted factors in knowledge system. Especially, after data cleaning, rules those have same form will be unite to one rule. But objects have different importance is more close to actually application. In this paper, weighted rough set (WRS) model is provided. WRS is based on variable precision rough set (VPRS) model. This model not only considers the noise tolerant capability, but also considers the objects' importance. In weighted rough set model, some basic concepts are redefined. Also, reduction definition is provided. At last, from the experiments, weighted rough set model's characters are got.