Speaker
Description
Missing data is a common challenge in data analysis, particularly in multi-omics studies, where heterogeneous data sources and technical limitations often result in incomplete measurements. In these situations, predictive models may fail when some required variables are missing.
This presentation demonstrates ensemble learning strategies designed to improve prediction despite incomplete data. The approach combines multiple base classifiers into a single framework, allowing different models to capture complementary patterns in the data.
The talk focuses on selecting a diverse set of classifiers that can generate predictions even when some variables are missing. Two strategies will be presented: static ensemble selection, which incorporates a modified classifier disagreement metric, and dynamic ensemble selection, which adapts the choice of models to the pattern of missing values in previously unseen samples.
The methodology is part of the author’s PhD research and contributed to the development of the playOmics R package.
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Additional Material or Paper
https://github.com/JagGlo/playOmics_env
| Keywords: Please list up to 5 keywords to help us find the right session for your contribution. | machine learning, statistical methods, multi-omics analysis, biomarkers discovery, disease diagnostics |
|---|---|
| Virtual Option | This submission is for onsite presentation only |
| Video Recording | Video sharing is fine |
| The author(s) agree(s) to take responsibility and be accountable for the contents of the submission and is/are authorized to present it. | Confirm |