Medizinische Universität Graz Austria/Österreich - Forschungsportal - Medical University of Graz
Gewählte Publikation:
SHR Neuro Krebs Kardio Lipid Stoffw Microb
Pfeifer, B; Kreuzthaler, M.
Calibrated kNN classification via second-layer neighborhood analysis
ADV DATA ANAL CLASSI. 2025;
Doi: 10.1007/s11634-025-00654-5
Web of Science
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- Führende Autor*innen der Med Uni Graz
- Kreuzthaler Markus Eduard
- Pfeifer Bastian
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- Abstract:
- The integration of artificial intelligence (AI) into medical practices has emphasized the importance of ensuring the reliability of predictive confidence, as it influences decision-making and the efficacy of AI-driven solutions. This paper focuses on the utilization of a non-parametric machine learning method, with a particular focus on the computation of confidence scores for individual classifications. Unlike parametric approaches, non-parametric techniques like k-nearest neighbors (kNN) offer flexibility without imposing strict data distribution assumptions. Leveraging this flexibility, we propose a novel kNN approach that introduces confidence-awareness through a two-layered neighborhood analysis. The developed approach is intended to support the classical non-parametric kNN classifier by providing more reliable and trustworthy class probabilities. Experimental evaluations conducted on benchmark datasets as well as a de-identified clinical real-world Electronic Health Records (EHR) data table consisting of thousands of unique class labels demonstrate the effectiveness of our approach in enhancing both, prediction accuracy and certainty assessment.
- Find related publications in this database (Keywords)
- Machine learning
- kNN
- Calibration
- Electronic Health Records