Investigating the Variation of Ensemble Size on Bagging-Based Classifier Performance in Imbalanced Bioinformatics Datasets

摘要

Bagging ensemble techniques have been utilized effectively by practitioners in the field of bioinformatics to alleviate the problem of class imbalance and to improve the performance of classification models. However, many previous works have used bagging only with a single arbitrary number of iterations. In this study, we raise the question of what is the impact of altering the number of iterations/ensembles on the classification performance of bagging classifiers? To answer this question, we conducted an empirical study using four different choices of number of iterations (10, 20, 50, and 100) within the bagging algorithm, across 15 different imbalanced bioinformatics datasets. Our results indicate that the choice of 50 iterations performs slightly better than all others without any exception, but the difference in performance is statistically insignificant. Thus, we recommend bagging with 10 iterations because, it achieves quality classification results, additional iterations do not significantly improve performance, and, a smaller number of iterations would be computationally less costly. The unique contribution of this work is to examine the effects of the number of iterations on the classification performance of bagging classifiers in the context of imbalanced datasets in the bioinformatics field.