An interpretable hypersphere information granule-based classifier for numeric data using axiomatic fuzzy set

Abstract The interpretability of classifiers focused on numerical data continues to pose significant challenges. This paper introduces an interpretable classifier rooted in axiomatic fuzzy set (AFS) theory and granular computing (GrC). The design of the proposed classifier consists of three stages:...
Ausführliche Beschreibung

Gespeichert in:
Autor*in:

Wang, Han-Shen [verfasserIn]

Lu, Wei [verfasserIn]

Format:

E-Artikel

Sprache:

Englisch

Erschienen:

2024

Schlagwörter:

Interpretable classifier

Axiomatic fuzzy set

Information granules

Anmerkung:

© The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Übergeordnetes Werk:

Enthalten in: Granular computing - Springer International Publishing, 2016, 9(2024), 3 vom: 21. Juni

Übergeordnetes Werk:

volume:9 ; year:2024 ; number:3 ; day:21 ; month:06

Links:

Volltext

DOI / URN:

10.1007/s41066-024-00488-0

Katalog-ID:

SPR056321155

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