Integrating Class Relation Knowledge in Probabilistic Learning Vector Quantization

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.