APAROS: A Clustering-Based Hybrid Approach for Handling Overlapped Regions in Imbalanced Datasets
In many real-world datasets, the class distribution is often highly imbalanced, and minority class samples are located within the majority regions, leading to significant overlap.
Annam Nandini +3 more
doaj +1 more source
Preprocessing vague imbalanced datasets and its use in genetic fuzzy classifiers
Ana M. Palacios +2 more
openalex +2 more sources
Imbalanced Dataset and Optimization Technique Use for Disease Treatment Default Prediction
Owusu-Adjei Michael +5 more
openalex +2 more sources
Facial Expression Recognition Based on Weighted-Cluster Loss and Deep Transfer Learning Using a Highly Imbalanced Dataset. [PDF]
Ngo QT, Yoon S.
europepmc +1 more source
Construction material classification on imbalanced datasets using Vision Transformer (ViT) architecture [PDF]
Maryam Soleymani +3 more
openalex +1 more source
Gaussian Based-SMOTE Method for Handling Imbalanced Small Datasets
Muhammad Misdram +4 more
openalex +1 more source
Fake Detection in Imbalance Dataset by Semi-Supervised Learning with GAN [PDF]
Jinus Bordbar +3 more
openalex +1 more source
Applying Minority Range to Gini Index to Handle Imbalanced Dataset in Decision Tree classifiers [PDF]
Marius Silaghi, Ben Mathew
openalex +1 more source
The effect of oversampling and undersampling on classifying imbalanced text datasets
Alexander Yun-chung Liu
openalex +1 more source
Machine Learning Approaches for the Prediction of Displaced Abomasum in Dairy Cows Using a Highly Imbalanced Dataset. [PDF]
Asgari Z +3 more
europepmc +1 more source

