Review of Bagging and Boosting Classification Performance on Unbalanced Binary Classification

Abstract

Quite a few times when the problem of study involves binary classification we are dealt with a situation of unbalanced class labels; the negative class often dominates the positive class leading to the problem that the model was not able to learn enough complexities to correctly classify the label which are lower in comparison. The Bagging and boosting classifiers in recent times have gained in popularity due to its robustness against the unbalanced class labels, both uses the notion of ensemble to generalize the model and predict on the unseen data. Through this paper we aim to explore the improvement in the classification performance by bagging and boosting classifiers on an unbalanced binary classification dataset.