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Identifying Highly Relevant Entries in Datasets: A Relevance-Based Classification

Author

Listed:
  • Fernando Delbianco

    (Departamento de Economía (UNS) - Instituto de Matemática de Bahía Blanca (CONICET))

  • Fernando Tohmé

    (Departamento de Economía (UNS) - Instituto de Matemática de Bahía Blanca (CONICET))

Abstract

In this paper, we present a methodology to classify dataset entries in datasets, based on their relevance for answering different specific queries. It employs a repeated individualized inference approach to identify entries with significant Shapley values, contributing with accurate answers to queries about other entries in the dataset. This information is captured in three matrices: a general relevance matrix, a Shapley value matrix, and a significant Shapley value matrix. Since usually the information in datasets is non-homogeneously distributed, relevance is often concentrated in a few entries. This is in particular observed in a representative case study.

Suggested Citation

  • Fernando Delbianco & Fernando Tohmé, 2025. "Identifying Highly Relevant Entries in Datasets: A Relevance-Based Classification," Journal of Classification, Springer;The Classification Society, vol. 42(3), pages 674-694, November.
    • Handle: RePEc:spr:jclass:v:42:y:2025:i:3:d:10.1007_s00357-025-09513-6
    DOI: 10.1007/s00357-025-09513-6
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    References listed on IDEAS

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    1. Delbianco Fernando & Tohmé Fernando, 2023. "What is a relevant control?: An algorithmic proposal," Asociación Argentina de Economía Política: Working Papers 4643, Asociación Argentina de Economía Política.
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    1. Delbianco Fernando & Tohmé Fernando, 2025. "Super-relevant synthetic data with individualized Shapley values," Asociación Argentina de Economía Política: Working Papers 4793, Asociación Argentina de Economía Política.

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