Abstract:In the generation of data classification prediction models, highly unbalanced training data will significantly degrade the performance of the model. Therefore, this study proposes an improved oversampling method for unbalanced data sets based on genetic ideas. Inspired by the chromosome theory of inheritance in biology, this method uses close relatives to generate similar but not identical new instances to balance the majority of classes. Under the premise of the same sample distribution, the bias influence of unbalanced data on the training results is reduced or even eliminated. Finally, a comparative experiment on a public data set shows that the method has achieved a higher recall rate and G-mean value, which proves that the improved method is effective and the comprehensive performance of the generated model has been promoted.