Turnitin
降AI改写
早检测系统
早降重系统
Turnitin-UK版
万方检测-期刊版
维普编辑部版
Grammarly检测
Paperpass检测
checkpass检测
PaperYY检测
Machine learning with oversampling and undersampling techniques: overview study and experimental results
R Mohammed, J Rawashdeh… - 2020 11th international …, 2020 - ieeexplore.ieee.org
Data imbalance in Machine Learning refers to an unequal distribution of classes within a
dataset. This issue is encountered mostly in classification tasks in which the distribution of …
dataset. This issue is encountered mostly in classification tasks in which the distribution 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 …
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 …
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 …
Machine learning assisted materials design and discovery for rechargeable batteries
Y Liu, B Guo, X Zou, Y Li, S Shi - Energy Storage Materials, 2020 - Elsevier
Abstract Machine learning plays an important role in accelerating the discovery and design
process for novel electrochemical energy storage materials. This review aims to provide the …
process for novel electrochemical energy storage materials. This review aims to provide the …
[HTML][HTML] Computer vision and machine learning for medical image analysis: recent advances, challenges, and way forward
The recent development in the areas of deep learning and deep convolutional neural
networks has significantly progressed and advanced the field of computer vision (CV) and …
networks has significantly progressed and advanced the field of computer vision (CV) and …
SMOTE for learning from imbalanced data: progress and challenges, marking the 15-year anniversary
The Synthetic Minority Oversampling Technique (SMOTE) preprocessing algorithm is
considered" de facto" standard in the framework of learning from imbalanced data. This is …
considered" de facto" standard in the framework of learning from imbalanced data. This is …
Learning from class-imbalanced data: Review of methods and applications
Rare events, especially those that could potentially negatively impact society, often require
humans' decision-making responses. Detecting rare events can be viewed as a prediction …
humans' decision-making responses. Detecting rare events can be viewed as a prediction …
On the class overlap problem in imbalanced data classification
Class imbalance is an active research area in the machine learning community. However,
existing and recent literature showed that class overlap had a higher negative impact on the …
existing and recent literature showed that class overlap had a higher negative impact on the …
[HTML][HTML] Learning from imbalanced data: open challenges and future directions
Despite more than two decades of continuous development learning from imbalanced data
is still a focus of intense research. Starting as a problem of skewed distributions of binary …
is still a focus of intense research. Starting as a problem of skewed distributions of binary …