Selecting the Appropriate Data Sampling Approach for Imbalanced and High-Dimensional Bioinformatics Datasets

摘要

One of the more prevalent problems when working with bioinformatics datasets is class imbalance, when there are more instances in one class compared to the other class (es). This problem is made worse because frequently, the class of interest is also the minority class. A possible solution is data sampling, a powerful tool for combating class imbalance by adding or removing instances to make the dataset more balanced. In addition to the choice of including data sampling, one of the most important decisions when applying data sampling is what the final class ratio should be. Commonly, the final class ratio when data sampling is applied is 50:50, however it is an open question whether other ratios are more appropriate for certain imbalanced datasets (all datasets in this paper have 25.16% minority instances or less) where a 50:50 ratio requires extreme modification to the dataset. In this work we compare six different data sampling approaches (feature selection with the pair wise combinations of three data sampling techniques and two final class ratios) with feature selection without data sampling with the goal of determining if the inclusion of data sampling is beneficial and if so, what should be the final class ratio. In order to test the six data sampling approaches and feature selection alone thoroughly, we utilize seven imbalanced and high-dimensional datasets, three feature selection techniques, and six classifiers. Our results show that for a majority of scenarios, random under sampling along with either 35:65 or 50:50 is the best data sampling approach. Statistical analysis shows that there is no significant difference between the data sampling approaches. However, despite this, we still recommend using random under sampling along with 35:65 as the final class ratio. This is because of the frequency of both random under sampling and 35:65 being the most frequent top performing data sampling technique and class ratio respectively. Additionally, 35:65 will hav- fewer negative impacts than 50:50 (less data loss or over fitting, which makes it a better choice if all other factors are equal) and random under sampling is more computationally efficient than any other form of sampling, including "no sampling" (both by not requiring any internal calculations and by producing a reduced, easier-to-work-with dataset). To our knowledge, this is the most comprehensive work which focuses on the choice of the inclusion and implementation of data sampling with different final class ratios on bioinformatics datasets which exhibit such large levels of class imbalance.