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
收藏
页码：552 / 565
页数：14
相关论文
共 50 条
  • [1] A rough set approach to data with missing attribute values
    Grzymala-Busse, Jerzy W.
    [J]. ROUGH SETS AND KNOWLEDGE TECHNOLOGY, PROCEEDINGS, 2006, 4062 : 58 - 67
  • [2] Rough set strategies to data with missing attribute values
    Grzymala-Busse, JW
    [J]. FOUNDATIONS AND NOVEL APPROACHES IN DATA MINING, 2006, 9 : 197 - 212
  • [3] PERFORMANCE ANALYSIS OF ROUGH SET-BASED IN THE CASE OF MISSING VALUES
    Nowicki, Robert K.
    Seliga, Robert
    Zelasko, Dariusz
    Hayashi, Yoichi
    [J]. JOURNAL OF ARTIFICIAL INTELLIGENCE AND SOFT COMPUTING RESEARCH, 2021, 11 (04) : 307 - 318
  • [4] MINING DATA WITH MISSING ATTRIBUTE VALUES: A COMPARISON OF PROBABILISTIC AND ROUGH SET APPROACHES
    Grzymala-Busse, J. W.
    [J]. INTELLIGENT DECISION MAKING SYSTEMS, VOL. 2, 2010, : 153 - +
  • [5] A comparison of traditional and rough set approaches to missing attribute values in data mining
    Grzymala-Busse, J. W.
    [J]. DATA MINING X: DATA MINING, PROTECTION, DETECTION AND OTHER SECURITY TECHNOLOGIES, 2009, 42 : 155 - 163
  • [6] Experiments on data with three interpretations of missing attribute values - A rough set approach
    Grzymala-Busse, Jerzy W.
    Santoso, Steven
    [J]. INTELLIGENT INFORMATION PROCESSING AND WEB MINING, PROCEEDINGS, 2006, : 143 - +
  • [7] A Comparison of Some Rough Set Approaches to Mining Symbolic Data with Missing Attribute Values
    Grzymala-Busse, Jerzy W.
    [J]. FOUNDATIONS OF INTELLIGENT SYSTEMS, 2011, 6804 : 52 - 61
  • [8] Adapting Fuzzy Rough Sets for Classification with Missing Values
    Lenz, Oliver Urs
    Peralta, Daniel
    Cornelis, Chris
    [J]. ROUGH SETS (IJCRS 2021), 2021, 12872 : 192 - 200
  • [9] The Application of Rough Set Technique for Missing Data
    Li, Bo
    Zhang, Erliang
    [J]. ADVANCED MEASUREMENT AND TEST, PARTS 1 AND 2, 2010, 439-440 : 1052 - +
  • [10] Rough-set-based ADR signaling from spontaneous reporting data with missing values
    Lin, Wen-Yang
    Lan, Lin
    Huang, Feng-Hsiung
    Wang, Min-Hsien
    [J]. JOURNAL OF BIOMEDICAL INFORMATICS, 2015, 58 : 235 - 246
12345