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

Fernando Delbianco () and Fernando Tohmé ()
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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)

Journal of Classification, 2025, vol. 42, issue 3, No 10, 674-694

Abstract: 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.

Keywords: Conformal prediction; Individualized inference; Synthetic data; Shapley (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s00357-025-09513-6

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