An empirical study on the effect of imbalanced data on bleeding detection in endoscopic video.

Abstract

In biomedical applications including classification of endoscopic videos, class imbalance is a common problem arising from the significant difference between the prior probabilities of different classes. In this paper, we investigate the performance of different classifiers for varying training data distribution in case of bleeding detection problem through three experiments. In the first experiment, we analyze the classifier performance for different class distribution with a fixed sized training dataset. The experiment provides the indication of the required class distribution for optimum classification performance. In the second and third experiments, we investigate the effect of both training data size and class distribution on the classification performance. From our experiments, we found that a larger dataset with moderate class imbalance yields better classification performance compared to a small dataset with balanced distribution. Ensemble classifiers are more robust to the variation in training dataset compared to single classifier.