WEIGHTED ENSEMBLE OF DIVERSIFIED SENSITIVITY-BASED UNDERSAMPLING FOR IMBALANCED PATTERN CLASSIFICATION PROBLEMS

The Diversified Sensitivity-based Undersampling (DSUS) is an undersampling method to solve the imbalance pattern classification problems which overcomes the drawbacks of ignoring the distribution information of the training dataset in random-based undersampling methods. The DSUS trains multiple neural networks during the undersampling process. However, only the final one is used. In this work, we propose a weighted ensemble method to improve the DSUS by using all trained neural networks with different weights according to their stochastic sensitivities. Experimental results 11 UCI datasets show that the proposed method outperforms the original DSUS.