Missing data imputation using fuzzy-rough methods

Mehran Amiri, Richard Jensen

Allbwn ymchwil: Cyfraniad at gyfnodolynErthygladolygiad gan gymheiriaid

118 Dyfyniadau(SciVal)
579 Wedi eu Llwytho i Lawr (Pure)


Missing values exist in many generated datasets in science. Therefore, utilizing missing data imputation methods is a common and important practice. These methods are a kind of treatment for uncertainty and vagueness existing in datasets. On the other hand, methods based on fuzzy-rough sets provide excellent tools for dealing with uncertainty, possessing highly desirable properties such as robustness and noise tolerance. Furthermore, they can find minimal representations of data and do not need potentially erroneous user inputs. As a result, utilizing fuzzy-rough sets for imputation should be an effective approach. In this paper, we propose three missing value imputation methods based on fuzzy-rough sets and its recent extensions; namely, implicator/t-norm based fuzzy-rough sets, vaguely quantified rough sets and also ordered weighted average based rough sets. These methods are compared against 11 state-of-the-art imputation methods implemented in the KEEL data mining software on 27 benchmark datasets. The results show, via non-parametric statistical analysis, that the proposed methods exhibit excellent
performance in general.
Iaith wreiddiolSaesneg
Tudalennau (o-i)152-164
Dyddiad ar-lein cynnar09 Mai 2016
Dynodwyr Gwrthrych Digidol (DOIs)
StatwsCyhoeddwyd - 12 Medi 2016

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