A Prediction for Classification of Highly Imbalanced Medical Dataset Using Databoost.IM with SVM
Recently, class imbalance problems have growing interest because of their classification difficulty caused by the imbalanced class distributions. In particular, many ensemble learning and machine learning methods have been proposed for classification of imbalance problem. However, these methods producing poor predictive accuracy of classification for two-class imbalanced dataset. In this paper, the authors propose a new approach that combines an ensemble-based learning algorithm with machine learning algorithm to improve the predictive power of classifiers for imbalanced liver data sets consisting of two classes.