Standard Decision Boundary in a Support-Domain of Fuzzy Classifier Prediction for the Task of Imbalanced Data Classification

Many real classification problems are characterized by a strong disturbance in a prior probability, which for the most of classification algorithms leads to favoring majority classes. The action most often used to deal with this problem is oversampling of the minority class by the smote algorithm. Following work proposes to employ a modification of an individual binary classifier support-domain decision boundary, similar to the fusion of classifier ensembles done by the Fuzzy Templates method to deal with imbalanced data classification without introducing any repeated or artificial patterns into the training set. The proposed solution has been tested in computer experiments, which results shows its potential in the imbalanced data classification.