The Influence of Unbalanced Economic Data on Feature Selection and Quality of Classifiers

Abstract

Research background: The successful learning of classifiers depends on the quality of data. Modeling is especially difficult when the data are unbalanced or contain many irrelevant variables. This is the case in many applications. The classification of rare events is the overarching goal, e.g. in bankruptcy prediction, churn analysis or fraud detection. The problem of irrelevant variables accompanies situations where the specification of the model is not known a priori, thus in typical conditions for data mining analysts.