@ARTICLE { Rsds3660,
author = {Shen, Lixiang and Tay, Francis E. H.}, 
title = {Economic and financial prediction using rough sets model}, 
journal = {European Journal of Operational Research}, 
number = 	{(no. 3)}, 
volume = 141, 
pages = {641-659}, 
publisher = {Elsevier Science Inc.}, 
address = {Amsterdam, The Netherlands}, 
month = {September}, 
year = 2002, 
issn = {0377-2217}, 
abstract = {A state-of-the-art review of the literature related to economic and financial prediction using rough sets model is presented, with special emphasis on the business failure prediction, database marketing and financial investment. These three applications require the accurate prediction of the future states based on the identification of patterns in the historical data. In addition, the historical data are in the format of a multi-attribute information table. All these conditions suit the rough sets model, an effective tool for multi-attribute classification problems. The different rough sets models and issues concerning the implementation of rough sets model - indicator selection, discretization and validation test, are also discussed in this paper. This paper will demonstrate that rough sets model is applicable to a wide range of practical problems pertaining to economic and financial prediction. In addition, the results show that the rough sets model is a promising alternative to the conventional methods for economic and financial prediction.}, 
keywords = {rough sets model, economic and financial prediction, }, 
}