Reliable Classification Learning for Medical Data Analysis Using Prototype-Based Models

Julius Voigt

doi:10.1007/978-3-032-03705-3_18

2026-01-01

PRE

DOI：10.1007/978-3-032-03705-3_18

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本论文展示了学习向量量化（LVQ）变体在医学领域可靠分类学习中的灵活性。更具体地说，我们利用知识引导的相关性学习，通过基因表达谱对乳腺癌亚型进行分类，其中关于基因共表达及其产生的通路和生物学过程的医学知识来源于医学数据库。这提高了可解释性，并允许从基因表达角度对那些关系对亚型识别的影响进行部分因果推断。此外，LVQ的相关性学习信息可用于检测和缓解数据中的不必要失真，从而有助于提高更可靠的分类能力。

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REJECTION STRATEGIES

期刊

ARTIFICIAL INTELLIGENCE AND SOFT COMPUTING, ICAISC 2025, PT I

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Mittweida University of Applied Sciences

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论文数: 18

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