Multicriteria optimized MLP for imbalanced learning

Classifier construction for data with imbalanced class frequencies needs special attention if good classification accuracy for all the classes is sought. When the classes are not separable, i.e., when the distributions of observations in the classes overlap, it is impossible to achieve ideal accuracy for all the classes at once. We suggest a versatile multicriteria optimization formulation for imbalanced classification and demonstrate its applicability using a single hidden layer perceptron as the classifier model.