[HTML][HTML] A review of ensemble learning and data augmentation models for class imbalanced problems: combination, implementation and evaluation
Class imbalance (CI) in classification problems arises when the number of observations
belonging to one class is lower than the other. Ensemble learning combines multiple models …
belonging to one class is lower than the other. Ensemble learning combines multiple models …
Imbalanced data classification based on diverse sample generation and classifier fusion
J Zhai, J Qi, S Zhang - International Journal of Machine Learning and …, 2022 - Springer
Class imbalance problems are pervasive in many real-world applications, yet classifying
imbalanced data remains to be a very challenging task in machine learning. SMOTE is the …
imbalanced data remains to be a very challenging task in machine learning. SMOTE is the …
A review on ensembles for the class imbalance problem: bagging-, boosting-, and hybrid-based approaches
Classifier learning with data-sets that suffer from imbalanced class distributions is a
challenging problem in data mining community. This issue occurs when the number of …
challenging problem in data mining community. This issue occurs when the number of …
Effective data generation for imbalanced learning using conditional generative adversarial networks
Learning from imbalanced datasets is a frequent but challenging task for standard
classification algorithms. Although there are different strategies to address this problem …
classification algorithms. Although there are different strategies to address this problem …
A survey of methods for managing the classification and solution of data imbalance problem
The problem of class imbalance is extensive for focusing on numerous applications in the
real world. In such a situation, nearly all of the examples are labeled as one class called …
real world. In such a situation, nearly all of the examples are labeled as one class called …
Sampling technique for noisy and borderline examples problem in imbalanced classification
Class imbalance Learning (CIL) is an important machine learning branch. Due to an
imbalanced dataset, the efficiency of the classifiers is impacted. Various under/oversampling …
imbalanced dataset, the efficiency of the classifiers is impacted. Various under/oversampling …
EHSO: Evolutionary Hybrid Sampling in overlapping scenarios for imbalanced learning
Y Zhu, Y Yan, Y Zhang, Y Zhang - Neurocomputing, 2020 - Elsevier
Imbalanced learning is a challenging task for conventional algorithms. Sampling techniques
address this problem by synthesizing minority class samples or selecting part of majority …
address this problem by synthesizing minority class samples or selecting part of majority …
SMOTE for handling imbalanced data problem: A review
Imbalanced class data distribution occurs when the number of examples representing one
class is much lower than others. This conditioning affects the prediction accuracy degraded …
class is much lower than others. This conditioning affects the prediction accuracy degraded …
Imbalance learning using heterogeneous ensembles
HG Zefrehi, H Altınçay - Expert Systems with Applications, 2020 - Elsevier
In binary classification, class-imbalance problem occurs when the number of samples in one
class is much larger than that of the other class. In such cases, the performance of a …
class is much larger than that of the other class. In such cases, the performance of a …
Hybrid methods for class imbalance learning employing bagging with sampling techniques
Class imbalance classification has become a dominant problem in supervised learning. The
bias of majority class instances dominates in quantity over minority class instances in …
bias of majority class instances dominates in quantity over minority class instances in …