Working Papers Series 2010, 2 : [1] Collection home page

Giovanna Menardi, Nicola Torelli
Training and assessing classification rules with unbalanced data

Browse
Collection's Items (Sorted by Submit Date in Descending order): 1 to 1 of 1
Issue DateTitleAuthor(s)
2010Training and assessing classification rules with unbalanced dataMenardi, Giovanna; Torelli, Nicola
Collection's Items (Sorted by Submit Date in Descending order): 1 to 1 of 1
Subscribe to this collection to receive daily e-mail notification of new additions RSS Feed RSS Feed RSS Feed

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 both the problems is proposed, based on a smoothed bootstrap re-sampling technique.