Particle swarm optimization-deep belief network-based rare class prediction model for highly class imbalance problem

Rare class imbalance problems, which involve the classification of minority or rare class, are difficult, because the size of the rare class is smaller than the majority class. Since majority class prediction is easy, its accuracy seems to be also high. However, the minority classes cannot be accurately predicted, and for this reason, when the prediction model performance is evaluated by considering only the accuracy, it does not indicate whether the model can predict the minority classes. Therefore, a rare class prediction technique is required. In this study, a rare class prediction model is proposed for minority class prediction. In addition, a dataset of a semiconductor manufacturing process with class imbalance problems was used to create a fault detection model. This prediction model uses data preprocessing to build the characteristics and data set required by the rare classes. To distinguish the rare classes related to the required characteristics, we used standard deviation and Euclidean distance to perform the feature selection. In addition, a particle swarm optimization-deep belief network was applied to create a classifier. The model proposed in this research presents outstanding performance and is appropriate for highly class imbalance problems.