Journal Article

Training and assessing classification rules with imbalanced data

Menardi, Giovanna, Torelli, Nicola

2014

Overview

The problem of modeling binary responses by using cross-sectional data has been addressed with a number of satisfying solutions that draw on both parametric and nonparametric methods. However, there exist many real situations where one of the two responses (usually the most interesting for the analysis) is rare. It has been largely reported that this class imbalance heavily compromises the process of learning, because the model tends to focus on the prevalent class and to ignore the rare events. However, not only the estimation of the classification model is affected by a skewed distribution of the classes, but also the evaluation of its accuracy is jeopardized, because the scarcity of data leads to poor estimates of the model’s accuracy. In this work, the effects of class imbalance on model training and model assessing are discussed. Moreover, a unified and systematic framework for dealing with the problem of imbalanced classification is proposed, based on a smoothed bootstrap re-sampling technique. The proposed technique is founded on a sound theoretical basis and an extensive empirical study shows that it outperforms the main other remedies to face imbalanced learning problems.

• Publisher: Springer US,Springer Nature B.V
• Subject: Accuracy / Artificial Intelligence / Chemistry and Earth Sciences / Computer Science / Data Mining and Knowledge Discovery / Discriminant analysis / Information Storage and Retrieval / Physics / Statistics for Engineering