On supervised class-imbalanced learning: An updated perspective and some key challenges
The problem of class imbalance has always been considered as a significant challenge to
traditional machine learning and the emerging deep learning research communities. A …
traditional machine learning and the emerging deep learning research communities. A …
An improving majority weighted minority oversampling technique for imbalanced classification problem
CR Wang, XH Shao - IEEE Access, 2020 - ieeexplore.ieee.org
Minority oversampling techniques have played a pivotal role in the field of imbalanced
learning. While traditional oversampling algorithms can cause problems such as intra-class …
learning. While traditional oversampling algorithms can cause problems such as intra-class …
Hybrid Approach with Distance Feature for Multi-Class Imbalanced Datasets
The multi-class imbalance problem has a higher level of complexity when compared to the
binary class problem. The difficulty is due to the large number of classes that will present …
binary class problem. The difficulty is due to the large number of classes that will present …
Class Imbalance Problem: A Wrapper-Based Approach using Under-Sampling with Ensemble Learning
Imbalanced data sets are a growing problem in data mining and business analytics.
However, the ability of machine learning algorithms to predict the minority class deteriorates …
However, the ability of machine learning algorithms to predict the minority class deteriorates …
Binary classification for imbalanced data using data conformity mechanism
J Zheng, S Ren, J Zhang, S Wang, L Li - Multimedia Systems, 2025 - Springer
It is valuable to explore those hidden patterns from imbalanced data. In imbalanced data,
skewed distribution of the classes makes minority classes to be hardly noticed. Existing …
skewed distribution of the classes makes minority classes to be hardly noticed. Existing …
An Adaptive Safe-Region Diversity Oversampling Algorithm for Imbalanced Classification
L Tao, H Li, F Wang, M Liu, Z Tang, QY Wang - IEEE Access, 2024 - ieeexplore.ieee.org
The challenge of imbalanced data classification stems from the uneven distribution of data
across classes, which is a formidable obstacle for traditional classifiers. Although numerous …
across classes, which is a formidable obstacle for traditional classifiers. Although numerous …
Oversampling for mining imbalanced datasets: Taxonomy and performance evaluation
P Jedrzejowicz - International Conference on Computational Collective …, 2022 - Springer
The paper focuses on methods and algorithms for oversampling two-classes imbalanced
datasets. We propose a taxonomy for oversampling approaches and review state-of-the-art …
datasets. We propose a taxonomy for oversampling approaches and review state-of-the-art …
Impact of Adaptive Synthetic on Naïve Bayes Accuracy in Imbalanced Anemia Detection Datasets
This research aims to analyze the impact of the Adaptive Synthetic (ADASYN) oversampling
technique on the performance of the Naïve Bayes classification algorithm on datasets with …
technique on the performance of the Naïve Bayes classification algorithm on datasets with …
A new oversampling method in the string space
VA Briones-Segovia, V Jiménez-Villar… - Expert Systems with …, 2021 - Elsevier
In syntactic and structural pattern recognition, data represented as strings appear in several
supervised classification applications. In some situations, data collections show imbalanced …
supervised classification applications. In some situations, data collections show imbalanced …
[PDF][PDF] Hybrid Approach with Distance Feature for Multi-Class Imbalanced Datasets
E Ongko - baraka.uma.ac.id
The multi-class imbalance problem has a higher level of complexity when compared to the
binary class problem. The difficulty is due to the large number of classes that will present …
binary class problem. The difficulty is due to the large number of classes that will present …