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A systematic study of online class imbalance learning with concept drift
As an emerging research topic, online class imbalance learning often combines the
challenges of both class imbalance and concept drift. It deals with data streams having very …
challenges of both class imbalance and concept drift. It deals with data streams having very …
Systematic review of class imbalance problems in manufacturing
A de Giorgio, G Cola, L Wang - Journal of Manufacturing Systems, 2023 - Elsevier
Class imbalance (CI) is a well-known problem in data science. Nowadays, it is affecting the
data modeling of many of the real-world processes that are being digitized. The …
data modeling of many of the real-world processes that are being digitized. The …
On the class overlap problem in imbalanced data classification
P Vuttipittayamongkol, E Elyan, A Petrovski - Knowledge-based systems, 2021 - Elsevier
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 …
[PDF][PDF] Classification with class imbalance problem
A Ali, SM Shamsuddin, AL Ralescu - Int. J. Advance Soft Compu …, 2013 - researchgate.net
Most existing classification approaches assume the underlying training set is evenly
distributed. In class imbalanced classification, the training set for one class (majority) far …
distributed. In class imbalanced classification, the training set for one class (majority) far …
What makes multi-class imbalanced problems difficult? An experimental study
M Lango, J Stefanowski - Expert Systems with Applications, 2022 - Elsevier
Multi-class imbalanced classification is more difficult and less frequently studied than its
binary counterpart. Moreover, research on the causes of the difficulty of multi-class …
binary counterpart. Moreover, research on the causes of the difficulty of multi-class …
Application of gradient boosting algorithms for anti-money laundering in cryptocurrencies
The recent emergence of cryptocurrencies has added another layer of complexity in the fight
towards financial crime. Cryptocurrencies require no central authority and offer pseudo …
towards financial crime. Cryptocurrencies require no central authority and offer pseudo …
Evolving diverse ensembles using genetic programming for classification with unbalanced data
In classification, machine learning algorithms can suffer a performance bias when data sets
are unbalanced. Data sets are unbalanced when at least one class is represented by only a …
are unbalanced. Data sets are unbalanced when at least one class is represented by only a …
Leveraging class balancing techniques to alleviate algorithmic bias for predictive tasks in education
Predictive modeling is a core technique used in tackling various tasks in learning analytics
research, eg, classifying educational forum posts, predicting learning performance, and …
research, eg, classifying educational forum posts, predicting learning performance, and …
Dealing with data difficulty factors while learning from imbalanced data
J Stefanowski - Challenges in computational statistics and data mining, 2015 - Springer
Learning from imbalanced data is still one of challenging tasks in machine learning and data
mining. We discuss the following data difficulty factors which deteriorate classification …
mining. We discuss the following data difficulty factors which deteriorate classification …
When is undersampling effective in unbalanced classification tasks?
A Dal Pozzolo, O Caelen, G Bontempi - Joint european conference on …, 2015 - Springer
A well-known rule of thumb in unbalanced classification recommends the rebalancing
(typically by resampling) of the classes before proceeding with the learning of the classifier …
(typically by resampling) of the classes before proceeding with the learning of the classifier …