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Relevance-based evaluation metrics for multi-class imbalanced domains

The class imbalance problem is a key issue that has received much attention. This attention has been mostly focused on two-classes problems. Fewer solutions exist for the multi-classes imbalance problem. From an evaluation point of view, the class …

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 is usually observed on the most rare and relevant cases for the user. This …

Resampling strategies for imbalanced time series

Time series forecasting is a challenging task, wherethe non-stationary characteristics of the data portrays a hardsetting for predictive tasks. A common issue is the imbalanceddistribution of the target variable, where some intervals are …

Crime prediction using regression and resources optimization

Violent crime is a well known social problem affecting both the quality of life and the economical development of a society. Its prediction is therefore an important asset for law enforcement agencies, since due to budget constraints, the …

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 studied thoroughly within machine learning. For regression tasks, where …