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[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 …
Converting nanotoxicity data to information using artificial intelligence and simulation
Decades of nanotoxicology research have generated extensive and diverse data sets.
However, data is not equal to information. The question is how to extract critical information …
However, data is not equal to information. The question is how to extract critical information …
A comparison of undersampling, oversampling, and SMOTE methods for dealing with imbalanced classification in educational data mining
Educational data mining is capable of producing useful data-driven applications (eg, early
warning systems in schools or the prediction of students' academic achievement) based on …
warning systems in schools or the prediction of students' academic achievement) based on …
A survey on imbalanced learning: latest research, applications and future directions
Imbalanced learning constitutes one of the most formidable challenges within data mining
and machine learning. Despite continuous research advancement over the past decades …
and machine learning. Despite continuous research advancement over the past decades …
DeepSMOTE: Fusing deep learning and SMOTE for imbalanced data
Despite over two decades of progress, imbalanced data is still considered a significant
challenge for contemporary machine learning models. Modern advances in deep learning …
challenge for contemporary machine learning models. Modern advances in deep learning …
SHAP and LIME: an evaluation of discriminative power in credit risk
In credit risk estimation, the most important element is obtaining a probability of default as
close as possible to the effective risk. This effort quickly prompted new, powerful algorithms …
close as possible to the effective risk. This effort quickly prompted new, powerful algorithms …
Effect of dataset size and train/test split ratios in QSAR/QSPR multiclass classification
Applied datasets can vary from a few hundred to thousands of samples in typical quantitative
structure-activity/property (QSAR/QSPR) relationships and classification. However, the size …
structure-activity/property (QSAR/QSPR) relationships and classification. However, the size …
Review of classification methods on unbalanced data sets
L Wang, M Han, X Li, N Zhang, H Cheng - Ieee Access, 2021 - ieeexplore.ieee.org
This paper studies the classification of unbalanced data sets. First, this kind of data sets is
briefly introduced, and then the classification methods of unbalanced data sets are analyzed …
briefly introduced, and then the classification methods of unbalanced data sets are analyzed …
Impact of SMOTE on imbalanced text features for toxic comments classification using RVVC model
Social media platforms and microblogging websites have gained accelerated popularity
during the past few years. These platforms are used for expressing views and opinions …
during the past few years. These platforms are used for expressing views and opinions …
Improving imbalanced learning through a heuristic oversampling method based on k-means and SMOTE
Learning from class-imbalanced data continues to be a common and challenging problem in
supervised learning as standard classification algorithms are designed to handle balanced …
supervised learning as standard classification algorithms are designed to handle balanced …