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Towards a unified model for symbolic knowledge extraction with hypercube-based methods

Authors :
Sabbatini, Federico
Ciatto, Giovanni
Calegari, Roberta
Omicini, Andrea
Source :
Intelligenza Artificiale; June 2023, Vol. 17 Issue: 1 p63-75, 13p
Publication Year :
2023

Abstract

The XAI community is currently studying and developing symbolic knowledge-extraction (SKE) algorithms as a means to produce human-intelligible explanations for black-box machine learning predictors, so as to achieve believability in human-machine interaction. However, many extraction procedures exist in the literature, and choosing the most adequate one is increasingly cumbersome, as novel methods keep on emerging. Challenges arise from the fact that SKE algorithms are commonly defined based on theoretical assumptions that typically hinder practical applicability. This paper focuses on hypercube-basedSKE methods, a quite general class of extraction techniques mostly devoted to regression-specific tasks. We first show that hypercube-based methods are flexible enough to support classification problems as well, then we propose a general model for them, and discuss how they support SKE on datasets, predictors, or learning tasks of any sort. Empirical examples are reported as well –based upon the PSyKE framework –, showing the applicability of hypercube-based methods to actual classification tasks.

Details

Language :
English
ISSN :
17248035
Volume :
17
Issue :
1
Database :
Supplemental Index
Journal :
Intelligenza Artificiale
Publication Type :
Periodical
Accession number :
ejs63268816
Full Text :
https://doi.org/10.3233/IA-230001