Research Article

Fuzzy data quality profiling with decision theoretic treatment of incomplete and inconsistent records

Authors

Bita Alipoor Morvarid Intelligent Industrial Systems Research Group, Iran.
Setayesh Zahiri Morvarid Intelligent Industrial Systems Research Group, Iran.
Seyyed Ahmad Edalatpanah * University College, Korea University, Seoul 02841, South Korea.
Duško Tešić Military Academy, University of Defence in Belgrade, 11042 Belgrade, Serbia.

https://doi.org/10.48313/uda.v3i3.112

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Abstract

Record-level data treatment requires a distinction between the degree of a quality defect and the loss attached to an intervention. We develop FQ-DTP, a fuzzy quality profiling and decision-theoretic framework that links completeness, consistency, and plausibility evidence to treatment actions. The general formulation separates graded memberships from latent-state probabilities and minimizes expected loss, with an optional query budget. Reported experiments evaluate a simpler five-action plug-in damage policy on an Adult subsample and three scikit-learn datasets with injected missingness and functional-dependency violations. At 30% Missing Not At Random (MNAR) missingness and 15% violations, mean cost-adjusted utility is 0.791, compared with 0.718 for mean/mode imputation, 0.710 for iterative-lite imputation, and 0.521 for a hard gate. Indicator-aware keep-all (0.882) and rule-based repair (0.844) remain stronger. Action agreement with an oracle using the same loss convention ranges from 83.6% to 97.4%, indicating internal agreement rather than external optimality. Ablations favour graded evidence and the specified asymmetric costs in this setting. The study does not evaluate the general posterior model, plausibility assessment, or query-budget formulation; predictive validation, clean-data calibration, query provenance, and clustered-seed inference require verification before deployment claims can be made.

Keywords

Fuzzy data quality Data profiling Decision theory Missing data Data cleaning Bayes risk

References (25)

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Published 2026-09-13
Issue Vol. 3 No. 3 (2026): Uncertainty Discourse and Applications
Section Articles
Document Type Research Article
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How to Cite

Alipoor, B., Zahiri, S., Edalatpanah, S. A., & Tešić, D. (2026). Fuzzy data quality profiling with decision theoretic treatment of incomplete and inconsistent records. Uncertainty Discourse and Applications, 3(3), 235-248. https://doi.org/10.48313/uda.v3i3.112

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