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 …
Evaluation of smote for high-dimensional class-imbalanced microarray data
L Lusa - 2012 11th international conference on machine …, 2012 - ieeexplore.ieee.org
Synthetic Minority Oversampling TEchnique (SMOTE) is a popular oversampling method
that was proposed to improve random oversampling but its behavior on high-dimensional …
that was proposed to improve random oversampling but its behavior on high-dimensional …
A novel oversampling technique for class-imbalanced learning based on SMOTE and natural neighbors
J Li, Q Zhu, Q Wu, Z Fan - Information Sciences, 2021 - Elsevier
Developing techniques for the machine learning of a classifier from class-imbalanced data
presents an important challenge. Among the existing methods for addressing this problem …
presents an important challenge. Among the existing methods for addressing this problem …
Smotefuna: Synthetic minority over-sampling technique based on furthest neighbour algorithm
Class imbalance occurs in classification problems in which the “normal” cases, or instances,
significantly outnumber the “abnormal” instances. Training a standard classifier on …
significantly outnumber the “abnormal” instances. Training a standard classifier on …
Lvq-smote–learning vector quantization based synthetic minority over–sampling technique for biomedical data
M Nakamura, Y Kajiwara, A Otsuka, H Kimura - BioData mining, 2013 - Springer
Background Over-sampling methods based on Synthetic Minority Over-sampling Technique
(SMOTE) have been proposed for classification problems of imbalanced biomedical data …
(SMOTE) have been proposed for classification problems of imbalanced biomedical data …
RCSMOTE: Range-Controlled synthetic minority over-sampling technique for handling the class imbalance problem
P Soltanzadeh, M Hashemzadeh - Information Sciences, 2021 - Elsevier
Abstract The Synthetic Minority Over-Sampling Technique (SMOTE) is one of the most well
known methods to solve the unequal class distribution problem in imbalanced datasets …
known methods to solve the unequal class distribution problem in imbalanced datasets …
A comprehensive analysis of synthetic minority oversampling technique (SMOTE) for handling class imbalance
D Elreedy, AF Atiya - Information Sciences, 2019 - Elsevier
Imbalanced classification problems are often encountered in many applications. The
challenge is that there is a minority class that has typically very little data and is often the …
challenge is that there is a minority class that has typically very little data and is often the …
Class prediction for high-dimensional class-imbalanced data
R Blagus, L Lusa - BMC bioinformatics, 2010 - Springer
Background The goal of class prediction studies is to develop rules to accurately predict the
class membership of new samples. The rules are derived using the values of the variables …
class membership of new samples. The rules are derived using the values of the variables …
SMOTE–IPF: Addressing the noisy and borderline examples problem in imbalanced classification by a re-sampling method with filtering
Classification datasets often have an unequal class distribution among their examples. This
problem is known as imbalanced classification. The Synthetic Minority Over-sampling …
problem is known as imbalanced classification. The Synthetic Minority Over-sampling …
Selective oversampling approach for strongly imbalanced data
Challenges posed by imbalanced data are encountered in many real-world applications.
One of the possible approaches to improve the classifier performance on imbalanced data is …
One of the possible approaches to improve the classifier performance on imbalanced data is …