Balancing Performance Measures in Classification Using Ensemble Learning Methods

Abstract

Ensemble learning methods have recently been widely used in various domains and applications owing to the improvements in computational efficiency and distributed computing advances. However, with the advent of wide variety of applications of machine learning techniques to class imbalance problems, further focus is needed to evaluate, improve and balance other performance measures such as sensitivity (true positive rate) and specificity (true negative rate) in classification. This paper demonstrates an approach to evaluate and balance the performance measures (specifically sensitivity and specificity) using ensemble learning methods for classification that can be especially useful in class imbalanced datasets. In this paper, ensemble learning methods (specifically bagging and boosting) are used to balance the performance measures (sensitivity and specificity) on a diabetes dataset to predict if a patient will be readmitted to the hospital based on various feature vectors. From the experiments conducted, it can be empirically concluded that, by using ensemble learning methods, although accuracy does improve to some margin, both sensitivity and specificity are balanced significantly and consistently over different cross validation approaches.