Knowledge reduction with its algorithm design based on improved rough entropy

Author

Yang Zhihui ; Yin Yunqiang

Author_Institution

Sch. of Math. & Informational Sci., East China Inst. of Technol., Fuzhou, China

Volume

2

fYear

2011

fDate

26-28 July 2011

Firstpage

1032

Lastpage

1037

Abstract

Knowledge reduction is an important problem in rough set theory. In this paper, an improved measurement is given to measure the roughness of rough set. Based on improved rough entropy, reduction theory and algorithm design are studied. Additionally, appling the weight´s idea in fuzzy theory, the conditional attribute weight in the decision table is investigated. Combing the conditional attribute weight with rough entropy, simple knowledge reduction algorithm and examples are given. Theoretical analysis and examples indicate that the complexity of this reduction algorithm is less than that based on the current positive region and the conditional information entropy.

Keywords

decision tables; entropy; fuzzy set theory; rough set theory; algorithm design; conditional attribute weight; decision table; fuzzy theory; knowledge reduction algorithm; reduction theory; rough entropy; rough set theory; Algorithm design and analysis; Approximation methods; Decision making; Entropy; Erbium; Information systems; Set theory; algorithm design; knowledge reduction; rough entropy; weight;

fLanguage

English

Publisher

ieee

Conference_Titel

Fuzzy Systems and Knowledge Discovery (FSKD), 2011 Eighth International Conference on

Conference_Location

Shanghai

Print_ISBN

978-1-61284-180-9

Type

conf

DOI

10.1109/FSKD.2011.6019627

Filename

6019627