@INPROCEEDINGS { Rsds3818,
author = {Slowinski, Roman}, 
title = {Rough Set Processing of Fuzzy Information}, 
booktitle = {Soft Computing}, 
conference = {International Workshop on Rough Sets and Soft Computing}, 
pages = {142--145}, 
publisher = {The Society for Computer Simulation}, 
address = {San Diego, U.S.A.}, 
month = {November}, 
year = 1994, 
editor = {Lin, Tsau Young and Wildberger, A.M.}, 
isbn = {1-56555-077-3}, 
abstract = {Information table contains a finite set of objects described by a finite set of attributes. If for each pair (object, attribute) there is known a finite set of values with corresponding degrees of credibility, i.e., a fuzzy set, then the information table is called fuzzy information table (FIT). A special case of the FIT is a table with single and crisp values for each pair (object, attribute). The letter has been used to represent decision situations analysed with the rough set concept. The analysis answers two basic questions related to decision situations: one about explanation of a present state and another about prescription of some future decisions. The aim of this paper is to generalize the rough set approach to the case of FIT. Fuzzy sets are used here for modelling of uncertainty and imprecision of attribute values coming from the following sources: interpretation of quantitative attributes in qualitative (linguistic) terms, imprecise measurement of attribute values, unknown (missing) attributes, multiple attribute values.}, 
keywords = {information table, rough sets, fuzzy sets, classification, decision rules, }, 
}