Explainable global error weighted on feature importance: The xGEWFI metric to evaluate the error of data imputation and data augmentation

Dessureault, J.-S. et Massicotte, D. (2023). Explainable global error weighted on feature importance: The xGEWFI metric to evaluate the error of data imputation and data augmentation. Applied Intelligence, 53 . 21532–21542. ISSN 1573-7497 DOI 10.1007/s10489-023-04661-x

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Résumé

Evaluating data imputation and augmentation performance is a critical issue in data science. In statistics, methods like Kolmogorov-Smirnov K-S test, Cramér-von Mises W2, Anderson- Darling A2, Pearson’s χ2 and Watson’s U2 exists for decades to compare the distribution of two datasets. In the context of data generation, typical evaluation metrics have the same flaw: They calculate the feature’s error and the global error on the generated data without weighting the error with the feature’s importance. In most cases, the importance of the features is imbalanced, and it can induce a bias on the features and global errors. This paper proposes a novel metric named “Explainable Global Error Weighted on Feature Importance” (xGEWFI). This new metric is tested in a whole preprocessing method that 1. Process the outliers, 2. impute the missing data, and 3. augments the data. At the end of the process, the xGEWFI error is calculated. The distribution error between the original and generated data is calculated using a Kolmogorov-Smirnov test (K-S test) for each feature. Those results are multiplied by the importance of the respective features and calculated using a Random Forest (RF) algorithm. The metric result is expressed in an explainable format, aiming for an ethical AI. This novel method provides a more precise evaluation of a data generation process than if only a K-S test were used.

Type de document:	Article
Mots-clés libres:	xGEWFI Data imputation Data augmentation Random forest SMOTE KNNImputer
Date de dépôt:	29 janv. 2024 13:51
Dernière modification:	06 juin 2024 04:00
Version du document déposé:	Post-print (version corrigée et acceptée)
URI:	https://depot-e.uqtr.ca/id/eprint/11094

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