The Random Forest Classifier in WEKA: Discussion and New Developments for Imbalanced Data
This work addresses classification issues for imbalanced datasets, particularly in medical studies, but is incremental as it adapts existing methods for a specific tool.
The authors tackled the problem of random forest classifiers performing poorly on imbalanced data, such as in medical studies, by proposing a balanced random forest approach for WEKA and showing it achieves superior prediction quality compared to unmodified random forest and reference implementations in R.
Data analysis and machine learning have become an integrative part of the modern scientific methodology, providing automated techniques to predict further information based on observations. One of these classification and regression techniques is the random forest approach. Those decision tree based predictors are best known for their good computational performance and scalability. However, in case of severely imbalanced training data, as often seen in medical studies' data with large control groups, the training algorithm or the sampling process has to be altered in order to improve the prediction quality for minority classes. In this work, a balanced random forest approach for WEKA is proposed. Furthermore, the prediction quality of the unmodified random forest implementation and the new balanced random forest version for WEKA are evaluated against reference implementations in R. Two-class problems on balanced data sets and imbalanced medical studies' data are investigated. A superior prediction quality using the proposed method for imbalanced data is shown compared to the other three techniques.