@INPROCEEDINGS { Rsds4515,
author = {Yao, Yiyu}, 
title = {Decision-Theoretic Rough Set Models}, 
booktitle = {Proceedings of the Second International Conference on Rough Sets and Knowledge Technology (RSKT'2007)}, 
conference = {Rough Sets and Knowledge Technology}, 
series =  = {Lecture Notes in Artificial Intelligence}, 
volume = 4481, 
pages = {1--12}, 
publisher = {Springer-Verlag}, 
address = {Heidelberg - Berlin, Germany}, 
year = 2007, 
editor = {Yao, Jing Tao and Lingras, Pawan and Wu, Wei-Zhi and Szczuka, Marcin and Cercone, N. and Slezak, Dominik}, 
isbn = {978-3-540-72457-5}, 
abstract = {Decision-theoretic rough set models are a probabilistic extension of the algebraic rough set model. The required parameters for defining probabilistic lower and upper approximations are calculated based on more familiar notions of costs (risks) through the well-known Bayesian decision procedure. We review and revisit the decision-theoretic models and present new results. It is shown that we need to consider additional issues in probabilistic rough set models.}, 
keywords = {bayesian decision theory, decision-theoretic rough sets, probabilistic rough sets, variable precision rough sets, }, 
}