@INPROCEEDINGS { Rsds4866,
author = {Delimata, Pawel and Suraj, Zbigniew}, 
title = {Algorithm for Feature Subsets Quality Boosting}, 
booktitle = {Proceedings of the Workshop on Concurrency, Specification and Programming (CS&P'2009)}, 
volume = 1, 
pages = {134--141}, 
publisher = {Warsaw University}, 
address = {Warsaw, Poland}, 
month = {September}, 
year = 2009, 
editor = {Czaja, Ludwik and Szczuka, Marcin}, 
isbn = {978-83-927816-1-5}, 
abstract = {In the paper the algorithm for feature quality boosting is presented. This algorithm allows increasing uality (in the classification meaning) of a set of attribute subsets of a given decision table. The algorithm also reduces the number of the resulting subsets. The set of attribute subsets created by this algorithm can later be used in the mul-tiple classifier construction. As an initial set of attribute subsets the algorithm uses a set of decision relative reducts or a set of decision and object relative reducts. The proposed algorithm have been tested on the benchmark data sets from the UCI Repository of Machine Learning Databases [1].}, 
keywords = {feature selection, decision relative reducts, decision and object relative reducts, multiple classifiers, }, 
}