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A broad review on class imbalance learning techniques
S Rezvani, X Wang - Applied Soft Computing, 2023 - Elsevier
The imbalanced learning issue is related to the performance of learning algorithms in the
presence of asymmetrical class distribution. Due to the complex characteristics of …
presence of asymmetrical class distribution. Due to the complex characteristics of …
A systematic review on imbalanced data challenges in machine learning: Applications and solutions
In machine learning, the data imbalance imposes challenges to perform data analytics in
almost all areas of real-world research. The raw primary data often suffers from the skewed …
almost all areas of real-world research. The raw primary data often suffers from the skewed …
Learning from multiple experts: Self-paced knowledge distillation for long-tailed classification
In real-world scenarios, data tends to exhibit a long-tailed distribution, which increases the
difficulty of training deep networks. In this paper, we propose a novel self-paced knowledge …
difficulty of training deep networks. In this paper, we propose a novel self-paced knowledge …
Intrusion detection of imbalanced network traffic based on machine learning and deep learning
L Liu, P Wang, J Lin, L Liu - IEEE access, 2020 - ieeexplore.ieee.org
In imbalanced network traffic, malicious cyber-attacks can often hide in large amounts of
normal data. It exhibits a high degree of stealth and obfuscation in cyberspace, making it …
normal data. It exhibits a high degree of stealth and obfuscation in cyberspace, making it …
Learning deep representation for imbalanced classification
Data in vision domain often exhibit highly-skewed class distribution, ie, most data belong to
a few majority classes, while the minority classes only contain a scarce amount of instances …
a few majority classes, while the minority classes only contain a scarce amount of instances …
Imbalanced deep learning by minority class incremental rectification
Model learning from class imbalanced training data is a long-standing and significant
challenge for machine learning. In particular, existing deep learning methods consider …
challenge for machine learning. In particular, existing deep learning methods consider …
[HTML][HTML] RN-SMOTE: Reduced Noise SMOTE based on DBSCAN for enhancing imbalanced data classification
A Arafa, N El-Fishawy, M Badawy, M Radad - Journal of King Saud …, 2022 - Elsevier
Abstract Machine learning classifiers perform well on balanced datasets. Unfortunately, a lot
of the real-world data sets are naturally imbalanced. So, imbalanced classification is a …
of the real-world data sets are naturally imbalanced. So, imbalanced classification is a …
Deep imbalanced learning for face recognition and attribute prediction
Data for face analysis often exhibit highly-skewed class distribution, ie, most data belong to
a few majority classes, while the minority classes only contain a scarce amount of instances …
a few majority classes, while the minority classes only contain a scarce amount of instances …
A survey of predictive modeling on imbalanced domains
P Branco, L Torgo, RP Ribeiro - ACM computing surveys (CSUR), 2016 - dl.acm.org
Many real-world data-mining applications involve obtaining predictive models using
datasets with strongly imbalanced distributions of the target variable. Frequently, the least …
datasets with strongly imbalanced distributions of the target variable. Frequently, the least …
Cost-sensitive learning of deep feature representations from imbalanced data
SH Khan, M Hayat, M Bennamoun… - IEEE transactions on …, 2017 - ieeexplore.ieee.org
Class imbalance is a common problem in the case of real-world object detection and
classification tasks. Data of some classes are abundant, making them an overrepresented …
classification tasks. Data of some classes are abundant, making them an overrepresented …