Variable Precision Rough Set Model in Information Tables with Missing Values | Zendy

Yoshifumi Kusunoki | Zendy; Masahiro Inuiguchi | Zendy

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Variable Precision Rough Set Model in Information Tables with Missing Values

Author(s) -

Yoshifumi Kusunoki,

Masahiro Inuiguchi

Publication year - 2011

Publication title -

journal of advanced computational intelligence and intelligent informatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.172

H-Index - 20

eISSN - 1343-0130

pISSN - 1883-8014

DOI - 10.20965/jaciii.2011.p0110

Subject(s) - rough set , generalization , set (abstract data type) , variable (mathematics) , computer science , missing data , dominance based rough set approach , data mining , function (biology) , similarity (geometry) , data set , interval (graph theory) , degree (music) , algorithm , mathematics , artificial intelligence , machine learning , combinatorics , image (mathematics) , mathematical analysis , physics , evolutionary biology , biology , acoustics , programming language

In this paper, we study rough set models in information tables with missing values. The variable precision rough set model proposed by Ziarko tolerates misclassification error using a membership function in complete information tables. We generalize the variable precision rough set in information tables with missing values. Because of incompleteness, the membership degree of each objects becomes an interval value. We define six different approximate regions using the lower and upper bounds of membership functions. The properties of the proposed rough set model are investigated. Moreover we show that the proposed model is a generalization of rough set models based on similarity relations.

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