@inbook{72d09fc10e6643bfb675b37aec63b9b5,

title = "Rough membership and Bayesian confirmation measures for parameterized rough sets",

abstract = "A generalization of the original idea of rough sets and variable precision rough sets is introduced. This generalization is based on the concept of absolute and relative rough membership. Similarly to variable precision rough set model, the generalization called parameterized rough set model, is aimed at modeling data relationships expressed in terms of frequency distribution rather than in terms of a full inclusion relation used in the classical rough set approach. However, differently from variable precision rough set model, one or more parameters modeling the degree to which the condition attribute values confirm the decision attribute value, are considered. The properties of this extended model are investigated and compared to the classical rough set model and the variable precision rough set model.",

author = "Salvatore Greco and B. Matarazzo and R. Slowinski",

year = "2005",

month = sep,

doi = "10.1007/11548669_33",

language = "English",

isbn = "9783540286530",

series = "Lecture Notes in Computer Science",

publisher = "Springer",

number = "3641",

pages = "314--324",

editor = "D. Slezak and G. Wang and M. Szczuka and I. Duntsch and Y. Yao",

booktitle = "Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing: Proceedings of the 10th International Conference. Vol. 1",

}