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The Use of Prediction Reliability Estimates on Imbalanced Datasets: A Case Study of Wall Shear Stress in the Human Carotid Artery Bifurcation
Abstract
Data mining techniques are extensively used on medical data, which is typically composed of many normal examples and few interesting ones. When presented with highly imbalanced data, some standard classifiers tend to ignore the minority class which leads to poor performance. Various solutions have been proposed to counter this problem. Random undersampling, random oversampling, and SMOTE (Synthetic Minority Oversampling Technique) are the most well-known approaches. In recent years several approaches to evaluate the reliability of single predictions have been developed. Most recently a simple and efficient approach, based on the classifier’s class probability estimates was shown to outperform the other reliability estimates. The authors propose to use this reliability estimate to improve the SMOTE algorithm. In this study, they demonstrate the positive effects of using the proposed algorithms on artificial datasets. The authors then apply the developed methodology on the problem of predicting the maximal wall shear stress (MWSS) in the human carotid artery bifurcation. The results indicate that it is feasible to improve the classifier’s performance by balancing the data with their versions of the SMOTE algorithm.
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