TY - JOUR
T1 - Missing data imputation using fuzzy-rough methods
AU - Amiri, Mehran
AU - Jensen, Richard
N1 - This is the author accepted manuscript. The final version is available from Elsevier via http://dx.doi.org/10.1016/j.neucom.2016.04.015
PY - 2016/9/12
Y1 - 2016/9/12
N2 - 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 excellentperformance in general.
AB - 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 excellentperformance in general.
KW - Missing value imputation
KW - fuzzy-rough sets
KW - OWA
KW - VQRS
UR - http://hdl.handle.net/2160/42954
U2 - 10.1016/j.neucom.2016.04.015
DO - 10.1016/j.neucom.2016.04.015
M3 - Article
SN - 0925-2312
VL - 205
SP - 152
EP - 164
JO - Neurocomputing
JF - Neurocomputing
ER -
