Rough Set Analysis of Classification Data with Missing Values

Marcin Szeląg; Jerzy Błaszczyński; Roman Słowiński

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

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Title

Rough Set Analysis of Classification Data with Missing Values

Authors

Marcin Szeląg (WI) ^{[ 1 ][ 2.3 ][ P ]}
Jerzy Błaszczyński (WI) ^{[ 1 ][ 2.3 ][ P ]}
Roman Słowiński ^{[ 2 ][ 2.3 ][ P ]}

^{[ 1 ]} Instytut Informatyki, Wydział Informatyki, Politechnika Poznańska | ^{[ 2 ]} Instytut Badań Systemowych PAN | ^{[ P ]} employee

Scientific discipline (Law 2.0)

[2.3] Information and communication technology

Year of publication

2017

Chapter type

chapter in monograph / paper

Publication language

english

Keywords

EN

Rough set
indiscernibility-based rough set approach
dominance-based rough set approach
missing values

Abstract

EN 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.

Date of online publication

22.06.2017

Pages (from - to)

552 - 565

DOI

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

URL

https://link.springer.com/chapter/10.1007/978-3-319-60837-2_44

Book

Rough Sets : International Joint Conference, IJCRS 2017, Olsztyn, Poland, July 3–7, 2017 : Proceedings, Part I

Presented on

International Joint Conference on Rough Sets IJCRS 2017, 3-7.07.2017, Olsztyn, Poland