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

Kubus Mariusz ()
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Kubus Mariusz: Opole University of Technology, Faculty of Production Engineering and Logistic, Department of Mathematics and Applied Computer Science, Sosnkowskiego 31, 45-272Opole, Poland

Folia Oeconomica Stetinensia, 2020, vol. 20, issue 1, 232-247

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.

Keywords: classifiers; class unbalance; sensitivity; feature selection; resampling (search for similar items in EconPapers)
JEL-codes: C1 C38 C52 (search for similar items in EconPapers)
Date: 2020
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Handle: RePEc:vrs:foeste:v:20:y:2020:i:1:p:232-247:n:14