New data level approach for imbalanced data classification improvement

Abstract

The article concerns the problem of imbalanced data classification. The algorithm improving a standard SMOTE method has been proposed and tested. It is a synergy of the existing approaches and was designed to be more versatile than other similar solutions. To measure the distance between objects, the Euclidean or the HVDM metrics were applied, depending on the number of nominal attributes in a data set.