Fig. 2

Overview of the Ensemble-Imbalance classifier proposed in this study. First, independent undersampled subsets are generated from the Training set using the Random Undersampling method. Then, classifiers created using a specific ML algorithm learn from these subsets (each classifier accesses only one subset). Finally, a majority voting strategy is used to classify patients into survival groups