Rough Set Analysis of Classification Data with Missing Values

被引：4

作者：

Szelag, Marcin ^{[1
]}

Blaszczynski, Jerzy ^{[1
]}

Slowinski, Roman ^{[1
,2
]}

机构：

[1] Poznan Univ Tech, Inst Comp Sci, PL-60965 Poznan, Poland

[2] Polish Acad Sci, Syst Res Inst, PL-01447 Warsaw, Poland

来源：

ROUGH SETS | 2017年 / 10313卷

关键词：

Rough set; Indiscernibility-based rough set approach; Dominance-based rough set approach; Missing values;

D O I：

10.1007/978-3-319-60837-2_44

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper, we consider a rough set analysis of non-ordinal and ordinal classification data with missing attribute values. We show how this problem can be addressed by several variants of Indiscernibility-based Rough Set Approach (IRSA) and Dominance-based Rough Set Approach (DRSA). We propose some desirable properties that a rough set approach being able to handle missing attribute values should possess. Then, we analyze which of these properties are satisfied by the considered variants of IRSA and DRSA.

引用

页码：552 / 565

页数：14

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