Ensemble vs. Data Sampling: Which Option Is Best Suited to Improve Classification Performance of Imbalanced Bioinformatics Data?

Abstract

Bioinformatics datasets contain challenging characteristics, such as class imbalance that occurs when one class has many more instances than the other class(es). These challenges make the task of classification much more subtle for practitioners and researchers in the field. Fortunately, there are tools, such as ensemble learning and data sampling methods that can be applied to overcome these problems and improve the performance of supervised classification models. Our motivation for this study is to investigate which option is well suited to tackle this significant challenge for bioinformatics data. Our literature survey shows that no previous work has conducted such an extensive study to examine whether ensemble learning or data sampling is best suited for imbalanced gene expression data. To this end, we carried out an extensive experimental study using five ensemble classification methods, four other classification methods with random under-sampling, three feature rankers along with four feature subset sizes across 15 highly imbalanced bioinformatics datasets. Our results along with statistical analysis confirm that ensemble learning methods in general outperform data sampling techniques in improving classification results. Furthermore, Select-Bagging with Naive Bayes (NB) followed by Random Forest are the top two performing ensemble techniques. Based on these results, we recommend either Select-Bagging with NB or Random Forest with 100 trees (RF100) for imbalanced datasets. However, RF100, unlike Select-Bagging, does not rely on choice of the base learner.