Ensemble SVM for imbalanced data and missing values in postoperative risk management

In this work, we propose the ensemble SVM that solves the problem of missing values of attributes and the imbalanced data phenomenon in the domain of postoperative risk management. Contrary to the other approaches the our solution effectively deals with the problems of high percentage of unknown values of the features. The problem of imbalanced data is solved by applying the cost-sensitive SVM as a base classifier of an ensemble, The quality of the proposed classifier is examined on a real-life dataset.