Knowledge acquisition based on rough set theory and principal component analysis

Author

Zeng, An ; Pan, Dan ; Zheng, Qi-Lun ; Peng, Hong

Author_Institution

Guangdong Univ. of Technol., Guangzhou, China

Volume

21

Issue

2

fYear

2006

Firstpage

78

Lastpage

85

Abstract

In this paper, we´ve developed a novel approach to knowledge acquisition based on rough set theory and principal component analysis. A PCA-based quantitative index measures the relative importance of different condition attributes among the state space constructed by all condition attributes. The index strengthens the attribute and attribute-value reductions while maintaining the decision table´s discernibility relations. Our KA-RSPCA algorithm outperformed four other RS algorithms on two test data sets.

Keywords

data reduction; decision tables; knowledge acquisition; principal component analysis; rough set theory; condition attributes; data reduction; decision table; knowledge acquisition; principal component analysis; quantitative index; rough set theory; Artificial intelligence; Heuristic algorithms; Knowledge acquisition; Knowledge based systems; Knowledge engineering; Mobile communication; Noise reduction; Principal component analysis; Set theory; State-space methods; collective correlation coefficient; knowledge acquisition; principal component analysis; rough set;

fLanguage

English

Journal_Title

Intelligent Systems, IEEE

Publisher

ieee

ISSN

1541-1672

Type

jour

DOI

10.1109/MIS.2006.32

Filename

1613824