Investigating a Different Approaches to Resolve Binary Classification Task with Unbalanced Dataset

Abstract

This work represents methods and approaches for training artificial intelligence when dataset is unbalanced. It helps to use existing dataset instead of generating new one which can be very hard and complex. To measure such methods specific performance metrics were provided. Methods and approaches were applied and results were generated.