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A systematic framework for preprocessing validation in business analytics
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AI
DOI：10.1080/2573234x.2026.2634271.png)
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Abstract
En 中文
PurposeRecent studies have identified methodological concerns in data science research, including unvalidated preprocessing assumptions that may contribute to inflated performance claims and irreproducible results. Despite preprocessing consuming 60-80% of analytical effort in business analytics projects, strategy selection typically relies on convention rather than systematic evidence. This study introduces REPROPREP, a methodological framework designed to enable systematic validation of preprocessing effectiveness assumptions.Design/methodology/approachThe REPROPREP framework incorporates conservative statistical analysis with Benjamini-Hochberg false discovery rate correction, systematic quality degradation protocols, and cost-effectiveness assessment. The framework was evaluated across 10 UCI repository datasets using gradient boosting classifiers with 5-fold stratified cross-validation under three simulated quality conditions, resulting in 90 statistical comparisons.FindingsThe analysis found no statistically significant differences between preprocessing strategies after multiple comparisons correction, with negligible effect sizes (mean AUC difference: 0.001). Cost analysis indicated implementation cost differences ranging from $150 to $800 across strategies.Practical implicationsThe framework provides a systematic methodology for preprocessing evaluation, enabling organizations to conduct context-specific validation of preprocessing effectiveness assumptions and support more informed decision-making regarding preprocessing strategies.Originality/valueREPROPREP introduces a reproducible and systematic approach to preprocessing validation that integrates statistical rigor with cost-benefit analysis. The framework is designed for adaptive development, with this initial release focusing on numeric preprocessing and providing a foundation for future extensions.
Keywords:
Preprocessing validation
methodological framework
business analytics
cost-effectiveness analysis
evidence-based methods
statistical validation
reproducibility
Journal
J
IF:
1.6
Papers:
14
Citations:
0