TY  - JOUR
A1  - Ultsch, Alfred
A1  - Lötsch, Jörn
T1  - Robust classification using posterior probability threshold computation followed by Voronoi cell based class assignment circumventing pitfalls of Bayesian analysis of biomedical data
T2  - International Journal of Molecular Sciences
N2  - Bayesian inference is ubiquitous in science and widely used in biomedical research such as cell sorting or “omics” approaches, as well as in machine learning (ML), artificial neural networks, and “big data” applications. However, the calculation is not robust in regions of low evidence. In cases where one group has a lower mean but a higher variance than another group, new cases with larger values are implausibly assigned to the group with typically smaller values. An approach for a robust extension of Bayesian inference is proposed that proceeds in two main steps starting from the Bayesian posterior probabilities. First, cases with low evidence are labeled as “uncertain” class membership. The boundary for low probabilities of class assignment (threshold 𝜀
) is calculated using a computed ABC analysis as a data-based technique for item categorization. This leaves a number of cases with uncertain classification (p < 𝜀
). Second, cases with uncertain class membership are relabeled based on the distance to neighboring classified cases based on Voronoi cells. The approach is demonstrated on biomedical data typically analyzed with Bayesian statistics, such as flow cytometric data sets or biomarkers used in medical diagnostics, where it increased the class assignment accuracy by 1–10% depending on the data set. The proposed extension of the Bayesian inference of class membership can be used to obtain robust and plausible class assignments even for data at the extremes of the distribution and/or for which evidence is weak.
KW  - data science
KW  - artificial intelligence
KW  - machine learning
KW  - digital medicine
Y1  - 2022
UR  - http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/75563
UR  - https://nbn-resolving.org/urn:nbn:de:hebis:30:3-755631
SN  - 1422-0067
VL  - 23.2022
IS  - 22
PB  - MDPI
CY  - Basel
ER  -