Novel Framework for Resources Optimization to Solve Class Imbalance Problems

In the actual world, AI is being used to address issues of class inequality. This is especially true when the information is not just unbalanced, but also multidimensional. When there is a class imbalance, a large dimensionality of datasets is always present, and both difficulties must be considered jointly. When using examples to evaluate each component, standard element picking algorithms usually provide equal weights to tests from different classes. As a result, they are unable to operate effectively with unbalanced data. When the costs of misclassification of different classes are different, cost-effective learning procedures are typically used. Different processes in writing have been established to deal with concerns related to class discomfort.