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Integrating Class Relation Knowledge in Probabilistic Learning Vector Quantization

Year: 2025

Type: conference-paper

Abstract: An interpretable approach to classification learning using cross-entropy loss is the Probabilistic Learning Vector Quantizer (PLVQ) as a robust prototype-based classifier.We propose a variant of the PLVQ, that allows the integration of domain knowledge.This strategy is becoming increasingly popular as a means of developing intelligent models that can enhance performance and gain acceptance from domain experts.In this paper, we put forth the idea of incorporating externally known class relations as supplementary information.We present theoretical aspects of the model and demonstrate its capabilities through numerical experiments.

Source: European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN)

DOI: 10.14428/esann/2025.es2025-64

Authors: Marika Kaden, Ronny Schubert, Tina Geweniger, Wieland Hermann, Thomas Villmann

Institutions: Hochschule Mittweida, Westsächsische Hochschule Zwickau, TU Bergakademie Freiberg

Language: English

Topic: Neural Networks and Applications

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: bronze

OpenAlex ID: W4409455380

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