Original title: Concept Drift Detection in Prediction Classifiers for Determining Gender in Metabolomics Analysis
Authors: Kostova, A. ; Schwarzerova, J.
Document type: Papers
Language: eng
Publisher: Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologií
Abstract: Currently, one of the most challenges in data analysis is connected to prediction modeling including dynamic information. Metabolomics analysis focuses on data presented dynamic information in real-time such as time-series data. Unfortunately, prediction models based on time series data are often affected by a phenomenon called concept drift. This phenomenon can reduce the accuracy of prediction models which is an unwanted effect. On the other hand, concept drift analysis can be useful in finding confounding factors. This study is divided into two parts. The first part presents the modeling of prediction classifiers based on metabolite data. The second part of this study brings concept drift detection in the created classified models. This study presented approaches to identify one of the confounding factors in human biology.
Keywords: Concept drift; Concept drift detection; Machine learning; Metabolomics; Prediction modeling
Host item entry: Proceedings I of the 28st Conference STUDENT EEICT 2022: General papers, ISBN 978-80-214-6029-4

Institution: Brno University of Technology (web)
Document availability information: Fulltext is available in the Brno University of Technology Digital Library.
Original record: http://hdl.handle.net/11012/209308

Permalink: http://www.nusl.cz/ntk/nusl-524735


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