A survey of predictive modeling on imbalanced domains
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 …
[HTML][HTML] The impact of class imbalance in classification performance metrics based on the binary confusion matrix
A major issue in the classification of class imbalanced datasets involves the determination of
the most suitable performance metrics to be used. In previous work using several examples …
the most suitable performance metrics to be used. In previous work using several examples …
[HTML][HTML] Key concepts, common pitfalls, and best practices in artificial intelligence and machine learning: focus on radiomics
B Koçak - Diagnostic and Interventional Radiology, 2022 - ncbi.nlm.nih.gov
Artificial intelligence (AI) and machine learning (ML) are increasingly used in radiology
research to deal with large and complex imaging data sets. Nowadays, ML tools have …
research to deal with large and complex imaging data sets. Nowadays, ML tools have …
[HTML][HTML] Optimal classifier for imbalanced data using Matthews Correlation Coefficient metric
Data imbalance is frequently encountered in biomedical applications. Resampling
techniques can be used in binary classification to tackle this issue. However such solutions …
techniques can be used in binary classification to tackle this issue. However such solutions …
SMOGN: a pre-processing approach for imbalanced regression
The problem of imbalanced domains, framed within predictive tasks, is relevant in many
practical applications. When dealing with imbalanced domains a performance degradation …
practical applications. When dealing with imbalanced domains a performance degradation …
GAN augmentation to deal with imbalance in imaging-based intrusion detection
Nowadays attacks on computer networks continue to advance at a rate outpacing cyber
defenders' ability to write new attack signatures. This paper illustrates a deep learning …
defenders' ability to write new attack signatures. This paper illustrates a deep learning …
[HTML][HTML] SMOTE for high-dimensional class-imbalanced data
R Blagus, L Lusa - BMC bioinformatics, 2013 - Springer
Background Classification using class-imbalanced data is biased in favor of the majority
class. The bias is even larger for high-dimensional data, where the number of variables …
class. The bias is even larger for high-dimensional data, where the number of variables …
Strength of stacking technique of ensemble learning in rockburst prediction with imbalanced data: Comparison of eight single and ensemble models
Rockburst is a common dynamic geological hazard, severely restricting the development
and utilization of underground space and resources. As the depth of excavation and mining …
and utilization of underground space and resources. As the depth of excavation and mining …
On the effectiveness of preprocessing methods when dealing with different levels of class imbalance
The present paper investigates the influence of both the imbalance ratio and the classifier on
the performance of several resampling strategies to deal with imbalanced data sets. The …
the performance of several resampling strategies to deal with imbalanced data sets. The …
Measuring and comparing the accuracy of species distribution models with presence–absence data
Species distribution models have been widely used to predict species distributions for
various purposes, including conservation planning, and climate change impact assessment …
various purposes, including conservation planning, and climate change impact assessment …