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

Michael Feldman, Adir Even, Yisrael Parmet

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

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.

Original languageEnglish
Pages (from-to)2643-2663
Number of pages21
JournalCommunications in Statistics - Theory and Methods
Volume47
Issue number11
DOIs
StatePublished - 3 Jun 2018

Keywords

  • Completeness
  • Data quality
  • Decision quality
  • Linear Discriminant Analysis (LDA)
  • Model quality

ASJC Scopus subject areas

  • Statistics and Probability

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