An insight into classification with imbalanced data: Empirical results and current trends on using data intrinsic characteristics
Training classifiers with datasets which suffer of imbalanced class distributions is an
important problem in data mining. This issue occurs when the number of examples …
important problem in data mining. This issue occurs when the number of examples …
Conditional Wasserstein generative adversarial network-gradient penalty-based approach to alleviating imbalanced data classification
In data mining, common classification algorithms cannot effectively learn from imbalanced
data. Oversampling addresses this problem by creating data for the minority class in order to …
data. Oversampling addresses this problem by creating data for the minority class in order to …
A classification method based on feature selection for imbalanced data
Y Liu, Y Wang, X Ren, H Zhou, X Diao - IEEE Access, 2019 - ieeexplore.ieee.org
Imbalanced data are very common in the real world, and it may deteriorate the performance
of the conventional classification algorithms. In order to resolve the imbalanced classification …
of the conventional classification algorithms. In order to resolve the imbalanced classification …
Application of machine learning and word embeddings in the classification of cancer diagnosis using patient anamnesis
Currently, one of the main challenges for information systems in healthcare is focused on
support for health professionals regarding disease classifications. This work presents an …
support for health professionals regarding disease classifications. This work presents an …
An automatic sampling ratio detection method based on genetic algorithm for imbalanced data classification
Imbalanced data are a common phenomenon in both theoretical research and real-world
applications. At a data level, standard classification algorithms cannot effectively learn and …
applications. At a data level, standard classification algorithms cannot effectively learn and …
A novel class imbalance-robust network for bearing fault diagnosis utilizing raw vibration signals
W Qian, S Li - Measurement, 2020 - Elsevier
Recently, although vast intelligent fault diagnosis methods are proposed, their validities are
mostly confirmed via balanced datasets, which cannot always hold for the class-imbalance …
mostly confirmed via balanced datasets, which cannot always hold for the class-imbalance …
Prediction of defective software modules using class imbalance learning
Software defect predictors are useful to maintain the high quality of software products
effectively. The early prediction of defective software modules can help the software …
effectively. The early prediction of defective software modules can help the software …
Comparison between statistical models and machine learning methods on classification for highly imbalanced multiclass kidney data
B Jeong, H Cho, J Kim, SK Kwon, SW Hong, CS Lee… - Diagnostics, 2020 - mdpi.com
This study aims to compare the classification performance of statistical models on highly
imbalanced kidney data. The health examination cohort database provided by the National …
imbalanced kidney data. The health examination cohort database provided by the National …