A New Rough set-based Heuristic Algorithm for Attribute Reduct

Author

Geng, Zhiqiang ; Zhu, Qunxiong

Author_Institution

Sch. of Inf. Sci. & Technol., Beijing Univ. of Chem. Technol.

Volume

1

fYear

0

fDate

0-0 0

Firstpage

3085

Lastpage

3089

Abstract

Learning algorithms of data mining are known to degrade in performance when faced with many attributes that are not necessary for rule discovery. Rough set theory has been a topic of general interest in the field of knowledge discovery. A new rough set-based greedy heuristic algorithm is proposed for attributes reduct and emphasized the role of basic constructs of rough set approach. The approach can select an optimal subset of attributes quickly and effectively from a large database with a lot of attributes. So the sensitivity of rough set to noise can be depressed and the system´s robustness is to be improved. The validity of the proposed algorithms is verified by comparing with genetic algorithms, Johnson´s algorithm and dynamic reducts in using practical machine learning databases

Keywords

data mining; greedy algorithms; heuristic programming; learning (artificial intelligence); rough set theory; attribute reduction; data mining; knowledge discovery; learning algorithms; machine learning databases; rough set-based greedy heuristic algorithm; rule discovery; Chemical technology; Clustering algorithms; Data mining; Databases; Degradation; Educational technology; Heuristic algorithms; Information science; Machine learning algorithms; Set theory; Attribute reduct; Heuristic algorithm; Knowledge discovery; Rough set;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Control and Automation, 2006. WCICA 2006. The Sixth World Congress on

Conference_Location

Dalian

Print_ISBN

1-4244-0332-4

Type

conf

DOI

10.1109/WCICA.2006.1712934

Filename

1712934