IMBoost: A New Weighting Factor for Boosting to Improve the Classification Performance of Imbalanced Data

Abstract

Imbalanced datasets pose significant challenges in the field of machine learning, as they consist of samples where one class (majority) dominates over the other class (minority). Although AdaBoost is a popular ensemble method known for its good performance in addressing various problems, it fails when dealing with imbalanced data sets due to its bias towards the majority class samples. In this study, we propose a novel weighting factor to enhance the performance of AdaBoost (called IMBoost). Our approach involves computing weights for both minority and majority class samples based on the performance of classifier on each class individually. Subsequently, we resample the data sets according to these new weights. To evaluate the effectiveness of our method, we compare it with six well-known ensemble methods on 30 imbalanced data sets and 4 synthetic data sets using ROC, precision-eecall AUC, and G-mean metrics. The results demonstrate the superiority of IMBoost. To further analyze the performance, we employ statistical tests, which confirm the excellence of our method.