Rough set theory for the treatment of incomplete data

Fulufhelo V. Nelwamondo; Tshilidzi Marwala

doi:10.1109/FUZZY.2007.4295389

Rough set theory for the treatment of incomplete data

Fulufhelo V. Nelwamondo
, Tshilidzi Marwala

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

21 Citations (Scopus)

Abstract

This paper proposes an algorithm based on rough set theory for missing data estimation. This paper also applies a rough set technique for missing data estimation to a large and real database for the first time. It is envisaged in this work that in large databases, it is more likely that the missing values could be correlated to some other variables observed somewhere in the same data. Instead of approximating missing data, it might be cheaper to identify indiscernibility relations between the observed data instances and those that contain missing attributes. Results obtained using the HIV database are acceptable with accuracies ranging from 74.7% to 100%. One drawback of this method is that it makes no extrapolation or interpolation and as a result, can only be used if the missing case is simmilar or related to another case with more observations.

Original language	English
Title of host publication	2007 IEEE International Conference on Fuzzy Systems, FUZZY
DOIs	https://doi.org/10.1109/FUZZY.2007.4295389
Publication status	Published - 2007
Externally published	Yes
Event	2007 IEEE International Conference on Fuzzy Systems, FUZZY - London, United Kingdom Duration: 23 Jul 2007 → 26 Jul 2007

Publication series

Name	IEEE International Conference on Fuzzy Systems
ISSN (Print)	1098-7584

Conference

Conference	2007 IEEE International Conference on Fuzzy Systems, FUZZY
Country/Territory	United Kingdom
City	London
Period	23/07/07 → 26/07/07

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

ASJC Scopus subject areas

Software
Theoretical Computer Science
Artificial Intelligence
Applied Mathematics

Access to Document

10.1109/FUZZY.2007.4295389

Cite this

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title = "Rough set theory for the treatment of incomplete data",

abstract = "This paper proposes an algorithm based on rough set theory for missing data estimation. This paper also applies a rough set technique for missing data estimation to a large and real database for the first time. It is envisaged in this work that in large databases, it is more likely that the missing values could be correlated to some other variables observed somewhere in the same data. Instead of approximating missing data, it might be cheaper to identify indiscernibility relations between the observed data instances and those that contain missing attributes. Results obtained using the HIV database are acceptable with accuracies ranging from 74.7\% to 100\%. One drawback of this method is that it makes no extrapolation or interpolation and as a result, can only be used if the missing case is simmilar or related to another case with more observations.",

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year = "2007",

doi = "10.1109/FUZZY.2007.4295389",

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series = "IEEE International Conference on Fuzzy Systems",

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AB - This paper proposes an algorithm based on rough set theory for missing data estimation. This paper also applies a rough set technique for missing data estimation to a large and real database for the first time. It is envisaged in this work that in large databases, it is more likely that the missing values could be correlated to some other variables observed somewhere in the same data. Instead of approximating missing data, it might be cheaper to identify indiscernibility relations between the observed data instances and those that contain missing attributes. Results obtained using the HIV database are acceptable with accuracies ranging from 74.7% to 100%. One drawback of this method is that it makes no extrapolation or interpolation and as a result, can only be used if the missing case is simmilar or related to another case with more observations.

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DO - 10.1109/FUZZY.2007.4295389

M3 - Conference contribution

AN - SCOPUS:50249131553

SN - 1424412102

SN - 9781424412105

T3 - IEEE International Conference on Fuzzy Systems

BT - 2007 IEEE International Conference on Fuzzy Systems, FUZZY

T2 - 2007 IEEE International Conference on Fuzzy Systems, FUZZY

Y2 - 23 July 2007 through 26 July 2007

ER -

Rough set theory for the treatment of incomplete data

Abstract

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Conference

UN SDGs

ASJC Scopus subject areas

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