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

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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)

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.

Suggested Citation

  • Kubus Mariusz, 2020. "The Influence of Unbalanced Economic Data on Feature Selection and Quality of Classifiers," Folia Oeconomica Stetinensia, Sciendo, vol. 20(1), pages 232-247, June.
    • Handle: RePEc:vrs:foeste:v:20:y:2020:i:1:p:232-247:n:14
    DOI: 10.2478/foli-2020-0014
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    References listed on IDEAS

    as
    1. King, Gary & Zeng, Langche, 2001. "Logistic Regression in Rare Events Data," Political Analysis, Cambridge University Press, vol. 9(2), pages 137-163, January.
    2. Hui Zou & Trevor Hastie, 2005. "Addendum: Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(5), pages 768-768, November.
    3. Hui Zou & Trevor Hastie, 2005. "Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(2), pages 301-320, April.
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    More about this item

    Keywords

    classifiers; class unbalance; sensitivity; feature selection; resampling;
    All these keywords.

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

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