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Proceedings of the 18th Conference on Computer Science and Intelligence Systems

Annals of Computer Science and Information Systems, Volume 35

Explainability in RIONA Algorithm Combining Rule Induction and Instance-Based Learning

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DOI: http://dx.doi.org/10.15439/2023F4139

Citation: Proceedings of the 18th Conference on Computer Science and Intelligence Systems, M. Ganzha, L. Maciaszek, M. Paprzycki, D. Ślęzak (eds). ACSIS, Vol. 35, pages 491–502 ()

Full text

Abstract. The article concerns the well-known RIONA algorithm. We focus on the explainability property of this algorithm. The theoretical results, formulated and proved in the paper, show the relationships of the RIONA classifiers to both instance- and rule-based classifiers. In particular, we show the equivalence (relative to the classification) of the RIONA algorithm with the rule-based algorithm generating all consistent and maximally general rules from the neighbourhood of the test case. Consequently, the RIONA classifier can be represented by a rule-based classifier, with rules easily interpretable by humans. These theoretical results provide the explainability of the classifiers generated by RIONA and could be used in situations when an explanation or justification of the derived decision is important. It should be noted that the RIONA algorithm requires analysing only a small number of objects and rules contrary to algorithms based on the generation of huge sets of rules.

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