@INPROCEEDINGS { Rsds4889,
author = {Prasad, P.S.V.S. Sai and Rao, Chillarige Raghavendra}, 
title = {IQuickReduct: An Improvement to Quick Reduct Algorithm}, 
booktitle = {Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing}, 
series =  = {Lecture Notes in Artificial Intelligence}, 
volume = 5908, 
pages = {152--159}, 
publisher = {Springer-Verlag}, 
address = {Heidelberg - Berlin, Germany}, 
year = 2009, 
editor = {Sakai, Hiroshi and Chakraborty, Mihir Kumar and Hassanien, Aboul Ella and Slezak, Dominik and Zhu, William}, 
url = {http://www.springerlink.com/content/r06323721k31/?p=7e2c1f1fe7ff42eb9b862fbed1bfd7d8&pi=19}, 
issn = {0302-9743}, 
isbn = {978-3-642-10645-3}, 
abstract = {Most of the Rough Sets applications are involved in conditional reduct computations. Quick Reduct Algorithm (QRA) for reduct computation is most popular since its discovery. The QRA has been modified in this paper by sequential redundancy reduction approach. The performance of this new improved Quick Reduct (IQRA) is discussed in this paper.}, 
keywords = {rough sets, feature selection, reduct, quick reduct, redundancy, variable precision rough sets, }, 
}