Machine learning algorithms such as genetic programming (GP) can evolve biased
classifiers when data sets are unbalanced. Data sets are unbalanced when at least one …

In recent years, increasingly severe wildfires have posed a significant threat to the safe and
stable operation of transmission lines. Wildfire risk assessment and early warning have …

The imbalance data refers to at least one of its classes which is usually outnumbered by the
other classes. The imbalanced data sets exist widely in the real world, and the classification …

The class imbalance problem usually occurs in real applications. The class imbalance is that
the amount of one class may be much less than that of another in training set. Under …

Class Imbalance Problem is the major issue in machine intelligence producing biased
classifiers that work well for the majority class but have a relatively poor performance for the …

Forests are essential natural resources that serve to facilitate economic activity while also
providing an essential impact on climate regulation and the carbon cycle. In the Chittagong …

The classification problem is one of the important research subjects in the field of machine
learning. However, most machine learning algorithms train a classifier based on the …

Frailty appears to be the most problematic expression of elderly people. Frail older adults
have a high risk of mortality, hospitalization, disability and other adverse outcomes, resulting …

Background Vancomycin trough concentration is closely associated with clinical efficacy and
toxicity. Predicting vancomycin trough concentrations in pediatric patients is challenging due …

For many data mining and machine learning applications predicting minority class samples
from skewed unbalanced data sets is a crucial problem. To address this problem, we …