Alterations to the Bootstrapping Process within Random Forest: A Case Study on Imbalanced Bioinformatics Data

Class imbalance is a significant challenge that practitioners in the field of bioinformatics are faced with on a daily basis. It is a phenomenon that occurs when number of instances of one class is much greater than number of instances of the other class(es) and it has adverse effects on the performance of classification models built on this skewed data. Random Forest as a robust classifier has been utilized effectively to deal with challenging characteristics of imbalanced bioinformatics datasets. In this study, we seek the answer to the question, do alterations to the bootstrapping process within Random Forest improve its classification performance? Thus, we performed an experimental study using Random Forest with four bootstrapping approaches, including two new novel bootstrapping approaches, across 15 imbalanced bioinformatics datasets. Our results demonstrate that two of the bootstrapping approaches, including one of our proposed approaches, outperform other approaches, however, this difference is statistically insignificant. We conclude that Random Forest is a robust classifier, able to handle the challenge of class imbalance, and can be slightly improved by altering bootstrapping process. To the best of our knowledge, no previous work has studied the effects of multiple bootstrapping processes on the performance of Random Forest in the domain of bioinformatics. In addition, we proposed and implemented the two innovative bootstrapping approaches evaluated in this paper.