Title :
Approach for absolute attribute reductions in rough sets
Author_Institution :
Coll. of Software, HuaZhong Univ. of Sci. & Technol., Wuhan
Abstract :
Attribute reductions is one of the basic contents in rough sets and one of the most problem in knowledge acquisition. At present, the methods used are often Pawlakpsilas data analysis and Skowronpsilas discernible matrix methods. In this paper an approach for the set of all absolute attribute reductions is proposed based on the discernible attribute set. Finally, we also show the experimental results by examples.
Keywords :
knowledge acquisition; rough set theory; absolute attribute reductions; data analysis; discernible matrix methods; knowledge acquisition; rough sets; Cybernetics; Data analysis; Educational institutions; Information systems; Knowledge acquisition; Machine learning; NP-hard problem; Rough sets; Set theory; Absolute attribute reduction; Rough set;
Conference_Titel :
Machine Learning and Cybernetics, 2008 International Conference on
Conference_Location :
Kunming
Print_ISBN :
978-1-4244-2095-7
Electronic_ISBN :
978-1-4244-2096-4
DOI :
10.1109/ICMLC.2008.4620437