Rough Set Model Based on Parameterized Probabilistic Similarity Relation in Incomplete Decision Tables

被引：0

作者：

Do Van, Nguyen ^{[1
]}

Yamada, Koichi ^{[1
]}

Unehara, Muneyuki ^{[1
]}

机构：

[1] Nagaoka Univ Technol, Dept Management & Informat Syst Sci, Nagaoka, Niigata, Japan

来源：

6TH INTERNATIONAL CONFERENCE ON SOFT COMPUTING AND INTELLIGENT SYSTEMS, AND THE 13TH INTERNATIONAL SYMPOSIUM ON ADVANCED INTELLIGENT SYSTEMS | 2012年

关键词：

Missing value; Incomplete decision tables; Rough Set; Set approximation; Tolerance relation; Similarity relation;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper discusses some extension of Rough set approach in Incomplete decision tables to deal with a problem of tolerance relation. Those approaches have been widely used to discover knowledge in incomplete information system. However, they also have their own limitation. In order to get more information from the relationship among objects, we propose a model called Parameterized Probabilistic Rough Set for incomplete decision tables. First we defined the probability of similarity between two objects if there is unavailable information. Then this probability is combined with a comparison based on available attribute values to derive a new relation.

引用

页码：577 / 582

页数：6

共 18 条

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