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 …
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 …
The harm of class imbalance corrections for risk prediction models: illustration and simulation using logistic regression
R van den Goorbergh, M van Smeden… - Journal of the …, 2022 - academic.oup.com
Objective Methods to correct class imbalance (imbalance between the frequency of outcome
events and nonevents) are receiving increasing interest for developing prediction models …
events and nonevents) are receiving increasing interest for developing prediction models …
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 …
Combining unsupervised and supervised learning in credit card fraud detection
Supervised learning techniques are widely employed in credit card fraud detection, as they
make use of the assumption that fraudulent patterns can be learned from an analysis of past …
make use of the assumption that fraudulent patterns can be learned from an analysis of past …
Credit card fraud detection: a realistic modeling and a novel learning strategy
Detecting frauds in credit card transactions is perhaps one of the best testbeds for
computational intelligence algorithms. In fact, this problem involves a number of relevant …
computational intelligence algorithms. In fact, this problem involves a number of relevant …
Enhanced credit card fraud detection model using machine learning
NS Alfaiz, SM Fati - Electronics, 2022 - mdpi.com
The COVID-19 pandemic has limited people's mobility to a certain extent, making it difficult
to purchase goods and services offline, which has led the creation of a culture of increased …
to purchase goods and services offline, which has led the creation of a culture of increased …
Calibrating probability with undersampling for unbalanced classification
Under sampling is a popular technique for unbalanced datasets to reduce the skew in class
distributions. However, it is well-known that under sampling one class modifies the priors of …
distributions. However, it is well-known that under sampling one class modifies the priors of …
Scarff: a scalable framework for streaming credit card fraud detection with spark
The expansion of the electronic commerce, together with an increasing confidence of
customers in electronic payments, makes of fraud detection a critical factor. Detecting frauds …
customers in electronic payments, makes of fraud detection a critical factor. Detecting frauds …
Corporate default forecasting with machine learning
We analyze the performance of a set of machine learning models in predicting default risk,
using standard statistical models, such as the logistic regression, as a benchmark. When …
using standard statistical models, such as the logistic regression, as a benchmark. When …