@INPROCEEDINGS { Rsds3080,
author = {Han, Jianchao and Hu, Xiaohua Tony and Cercone, Nick}, 
title = {Supervised Learning: A Generalized Rough Set Approach}, 
booktitle = {Rough Sets and Current Trends in Computing}, 
conference = {Rough Sets and Current Trends in Computing}, 
series =  = {Lecture Notes in Artificial Intelligence}, 
volume = 2005, 
pages = {322--329}, 
publisher = {Springer-Verlag}, 
address = {Heidelberg - Berlin, Germany}, 
year = 2001, 
editor = {Ziarko, Wojciech and Yao, Yiyu}, 
issn = {0302-9743}, 
isbn = {3-540-43074-1}, 
abstract = {Classification rules induction is a central problem addressed by machine learning and data mining. Rough sets theory is an important tool for data classification. Traditional rough sets approach, however, pursuits the fully correct or certain classification rules without considering other factors such as uncertain class labeling, importance of examples, as well as the uncertainty of the final rules. A generalized rough sets model, GRS, is proposed and a classification rules induction approach based on GRS is suggested. Our approach extends the variable precision rough sets model and attempts to reduce the influence of noise by considering the importance of each training example and handling the uncertain class labels. The final classification rules are also measured with the uncertainty factor.}, 
keywords = {rough set theory (RST), supervised learning, classification, rule induction, }, 
}