Individual-specific analysis indicates that goat- and lamb-like individuals can benefit the most from adaptation to the operational imbalance.
For instance, Skew-Sensitive Boolean combination (SSBC) is a active approach that estimates target vs. non-target proportions periodically during operations using Hellinger distance, and adapts its ensemble fusion function to operational class imbalance.
According to Doublet et al. (1995), poor oil and/or gas reservoir studies can result in operational problems as well as economic imbalance, such as Low reservoir energy.
This approach allows one to adapt pre-computed histograms and ensemble fusion functions based on the imbalance and complexity of operational data.
This fact is directly related to the proportional imbalance between the project members' operational and strategic responsibilities in terms of technology application and development, i.e., representatives of industrial partners showed the greatest degree of adherence to operational functionality, representatives of academic partners to the strategic.
Error forecasts of intermittent generation of renewable energy sources (RES) along with the current inelastic behavior of the consumption can provoke considerable network operational issues, such as frequency fluctuations and voltage imbalances.
Even with all of the spatial uncertainties included, an imbalance of ~20 W m−2 still existed, perhaps due to other instrumental, operational, and flow based errors (Finnigan 2008).
The characteristics of an EOWP depend on the power generation of the wind farm and its coupling with the day-ahead market such as the imbalance of the wind farm with the scheduled wind generation in the operational phase.
Then, during operations, the closest adjacent levels of class imbalance are periodically estimated using the Hellinger distance between the data distribution of inputs and that of imbalance levels, and used to approximate the most accurate BC of classifiers from operational points of these curves.
Several ensemble-based techniques have been proposed to design pattern recognition systems when data has imbalanced class distributions, although class proportions may change over time according to the operational environment.
