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A methodology for quantifying the effect of missing data on decision quality in classification problems

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  • Michael Feldman
  • Adir Even
  • Yisrael Parmet

Abstract

Decision making is often supported by decision models. This study suggests that the negative impact of poor data quality (DQ) on decision making is often mediated by biased model estimation. To highlight this perspective, we develop an analytical framework that links three quality levels – data, model, and decision. The general framework is first developed at a high-level, and then extended further toward understanding the effect of incomplete datasets on Linear Discriminant Analysis (LDA) classifiers. The interplay between the three quality levels is evaluated analytically – initially for a one-dimensional case, and then for multiple dimensions. The impact is then further analyzed through several simulative experiments with artificial and real-world datasets. The experiment results support the analytical development and reveal nearly-exponential decline in the decision error as the completeness level increases. To conclude, we discuss the framework and the empirical findings, elaborate on the implications of our model on the data quality management, and the use of data for decision-models estimation.

Suggested Citation

  • Michael Feldman & Adir Even & Yisrael Parmet, 2018. "A methodology for quantifying the effect of missing data on decision quality in classification problems," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 47(11), pages 2643-2663, June.
  • Handle: RePEc:taf:lstaxx:v:47:y:2018:i:11:p:2643-2663
    DOI: 10.1080/03610926.2016.1277752
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    Cited by:

    1. Bernd Heinrich & Marcus Hopf & Daniel Lohninger & Alexander Schiller & Michael Szubartowicz, 2021. "Data quality in recommender systems: the impact of completeness of item content data on prediction accuracy of recommender systems," Electronic Markets, Springer;IIM University of St. Gallen, vol. 31(2), pages 389-409, June.
    2. Bernd Heinrich & Marcus Hopf & Daniel Lohninger & Alexander Schiller & Michael Szubartowicz, 2022. "Something’s Missing? A Procedure for Extending Item Content Data Sets in the Context of Recommender Systems," Information Systems Frontiers, Springer, vol. 24(1), pages 267-286, February.

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