[HTML][HTML] SMOTE for high-dimensional class-imbalanced data
R Blagus, L Lusa - BMC bioinformatics, 2013 - Springer
Background Classification using class-imbalanced data is biased in favor of the majority
class. The bias is even larger for high-dimensional data, where the number of variables …
class. The bias is even larger for high-dimensional data, where the number of variables …
[HTML][HTML] SMOTE for high-dimensional class-imbalanced data
L Lusa - BMC Bioinformatics, 2013 - ncbi.nlm.nih.gov
Background Classification using class-imbalanced data is biased in favor of the majority
class. The bias is even larger for high-dimensional data, where the number of variables …
class. The bias is even larger for high-dimensional data, where the number of variables …
SMOTE for high-dimensional class-imbalanced data
L Lusa - BMC Bioinformatics, 2013 - go.gale.com
Background The objective of class prediction (classification) is to develop a rule based on a
group of samples with known class membership (training set), which can be used to assign …
group of samples with known class membership (training set), which can be used to assign …
SMOTE for high-dimensional class-imbalanced data.
R Blagus, L Lusa - BMC Bioinformatics, 2013 - search.ebscohost.com
Background: Classification using class-imbalanced data is biased in favor of the majority
class. The bias is even larger for high-dimensional data, where the number of variables …
class. The bias is even larger for high-dimensional data, where the number of variables …
[引用][C] SMOTE for high‐dimensional class‐imbalanced data
R Blagus - BMC Bioinformatics, 2013 - cir.nii.ac.jp
SMOTE for high-dimensional class-imbalanced data
L Lusa - BMC bioinformatics, 2013 - pubmed.ncbi.nlm.nih.gov
Background Classification using class-imbalanced data is biased in favor of the majority
class. The bias is even larger for high-dimensional data, where the number of variables …
class. The bias is even larger for high-dimensional data, where the number of variables …
SMOTE for high-dimensional class-imbalanced data.
R Blagus, L Lusa - BMC Bioinformatics, 2013 - europepmc.org
Background Classification using class-imbalanced data is biased in favor of the majority
class. The bias is even larger for high-dimensional data, where the number of variables …
class. The bias is even larger for high-dimensional data, where the number of variables …
[HTML][HTML] SMOTE for high-dimensional class-imbalanced data
R Blagus, L Lusa - BMC Bioinformatics, 2013 - bmcbioinformatics.biomedcentral …
Classification using class-imbalanced data is biased in favor of the majority class. The bias
is even larger for high-dimensional data, where the number of variables greatly exceeds the …
is even larger for high-dimensional data, where the number of variables greatly exceeds the …
SMOTE for high-dimensional class-imbalanced data
L Lusa - BMC Bioinformatics, 2013 - search.proquest.com
Background: Classification using class-imbalanced data is biased in favor of the majority
class. The bias is even larger for high-dimensional data, where the number of variables …
class. The bias is even larger for high-dimensional data, where the number of variables …
[引用][C] SMOTE for high-dimensional class-imbalanced data
R Blagus - BMC Bioinformatics, 2013 - cir.nii.ac.jp