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We present a preliminary study of a model-agnostic method called prototype pair decomposition that generates simple and accurate decision rules from datasets. The research focuses on its application to decision trees. It starts by selecting representative prototypes obtained by a prototype construction method, then pairs of prototypes from opposite classes are determined. These pairs define subspaces containing a fragment of the decision boundary in which a shallow decision tree is applied to extract simple decision rules consisting of a few premises. The results indicate that the proposed solution allows the extraction of locally competent simple rules that are comparable in terms of classification accuracy to a large and complex set of global rules obtained from standard decision trees.

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Prototype-Pairs Decomposition for Extracting Simple and Meaningful Rules

  • 会议论文

  • 发表日期：

    2025年

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  • Marcin Blachnik,
  • Mirosław Kordos,
  • Daniel Dąbrowski

摘要

We present a preliminary study of a model-agnostic method called prototype pair decomposition that generates simple and accurate decision rules from datasets. The research focuses on its application to decision trees. It starts by selecting representative prototypes obtained by a prototype construction method, then pairs of prototypes from opposite classes are determined. These pairs define subspaces containing a fragment of the decision boundary in which a shallow decision tree is applied to extract simple decision rules consisting of a few premises. The results indicate that the proposed solution allows the extraction of locally competent simple rules that are comparable in terms of classification accuracy to a large and complex set of global rules obtained from standard decision trees.