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[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 …
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 …
A survey on imbalanced learning: latest research, applications and future directions
Imbalanced learning constitutes one of the most formidable challenges within data mining
and machine learning. Despite continuous research advancement over the past decades …
and machine learning. Despite continuous research advancement over the past decades …
Technical analysis strategy optimization using a machine learning approach in stock market indices
Within the area of stock market prediction, forecasting price values or movements is one of
the most challenging issue. Because of this, the use of machine learning techniques in …
the most challenging issue. Because of this, the use of machine learning techniques in …
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 …
Smote for regression
Several real world prediction problems involve forecasting rare values of a target variable.
When this variable is nominal we have a problem of class imbalance that was already …
When this variable is nominal we have a problem of class imbalance that was already …
Geometric SMOTE for regression
Learning from imbalanced data sets is known to be a challenging task. There are many
proposals to tackle the challenge for classification problems, but regarding regression the …
proposals to tackle the challenge for classification problems, but regarding regression the …
[PDF][PDF] Evaluation of SVM performance in the detection of lung cancer in marked CT scan dataset
HF Kareem, MS AL-Husieny… - Indonesian Journal …, 2021 - pdfs.semanticscholar.org
This paper concerns the development/analysis of the IQ-OTH/NCCD lung cancer dataset.
This CT-scan dataset includes more than 1100 images of diagnosed healthy and tumorous …
This CT-scan dataset includes more than 1100 images of diagnosed healthy and tumorous …
Resampling strategies for regression
Several real world prediction problems involve forecasting rare values of a target variable.
When this variable is nominal, we have a problem of class imbalance that was thoroughly …
When this variable is nominal, we have a problem of class imbalance that was thoroughly …
Pre-processing approaches for imbalanced distributions in regression
Imbalanced domains are an important problem frequently arising in real world predictive
analytics. A significant body of research has addressed imbalanced distributions in …
analytics. A significant body of research has addressed imbalanced distributions in …