Imbalanced domains are an important problem frequently arising in real world predictive analytics. A significant body of research has addressed imbalanced distributions in classification tasks, where the target variable is nominal. In the context of …
Imbalanced domains are an important problem that arises in predictive tasks causing a loss in the performance on the most relevant cases for the user. This problem has been extensively studied for classification problems, where the target variable is …
Time series forecasting is a challenging task, where the non-stationary characteristics of data portray a hard setting for predictive tasks. A common issue is the imbalanced distribution of the target variable, where some values are very important to …
Social media is rapidly becoming the main source of news consumption for users, raising significant challenges to news aggregation and recommendation tasks. One of these challenges concerns the recommendation of very recent news. To tackle this …
Many real-world data-mining applications involve obtaining predictive models using datasets with stronglyimbalanced distributions of the target variable. Frequently, the least-common values of this target variableare associated with events that are …
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 studied within machine learning. For regression tasks, where the …