@INPROCEEDINGS { Rsds161,
author = {Khoo, Li-pheng and Tor, S.B.}, 
title = {A Rough-Set-Based Approach for Classification and Rule Induction}, 
booktitle = {The International Journal of Advanced Manufacturing Technology}, 
pages = {438-444}, 
publisher = {Springer-Verlag}, 
address = {Heidelberg - Berlin, Germany}, 
month = {June}, 
year = 1999, 
issn = {0268-3768}, 
abstract = {The inconsistency of information about objects may be the greatest obstacle to performing inductive learning from examples. Rough sets theory provides a new mathematical tool to deal with uncertainty and vagueness. Based on rough sets theory, this paper proposes a novel approach for the classification and rule induction of inconsistent information systems. It is achieved by integrating rough sets theory with a statistics-based inductive learning algorithm. The framework of a proto-type rough-set-based classification system (RClass) is presented. Two examples are used to verify the prototype system. The results of the validation are discussed.}, 
keywords = {artificial intelligence, automatic classification, machine learning, rough sets, }, 
}