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The article presents an algorithm supporting the detection of errors in databases. Unlike typical approaches, where correctness rules are defined a priori, in the proposed approach, they are constructed adaptively based on the statistical analysis of historical data. A huge number of automatically generated potential features are first statistically analyzed using the kernel method, and then, similarity indices between features are determined using the Rajski/Jaccard entropy-based metric. The algorithm ensures a balance between the individual usefulness of the features and their independence from others. A low-dimensional feature vector is obtained, which is the input to a classifier based on a neural network. The results of comparative experimental studies are presented, and the usefulness of the algorithm in practical application is discussed.

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Entropic Metrics in Feature Selection for Neural Network-Based Data Error Detection in CMDB

  • 会议论文

  • 发表日期：

    2026年

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  • Grzegorz Mzyk,
  • Szymon Niewiadomski

摘要

The article presents an algorithm supporting the detection of errors in databases. Unlike typical approaches, where correctness rules are defined a priori, in the proposed approach, they are constructed adaptively based on the statistical analysis of historical data. A huge number of automatically generated potential features are first statistically analyzed using the kernel method, and then, similarity indices between features are determined using the Rajski/Jaccard entropy-based metric. The algorithm ensures a balance between the individual usefulness of the features and their independence from others. A low-dimensional feature vector is obtained, which is the input to a classifier based on a neural network. The results of comparative experimental studies are presented, and the usefulness of the algorithm in practical application is discussed.