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Reliable Classification Learning for Medical Data Analysis Using Prototype-Based Models
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DOI：10.1007/978-3-032-03705-3_18.png)
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Abstract
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In this contribution we demonstrate the flexibility of learning vector quantization (LVQ) variants for reliable classification learning in a medical domain. More specifically, we consider classification of breast cancer subtypes by means of gene expression profiles using knowledge-informed relevance learning, where the medical knowledge regarding coexpression of genes and resulting pathways and biological processes are obtained from medical databases. This leads to better interpretability and allows partial causal inferences about the influence of those relations to identification of the subtypes from a gene-expression perspective. Further, relevance learning information from LVQ can be used to detect and to mitigate unwanted distortion from the data and, hence, contributes to more reliable classification ability.
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