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KOŠTOVAL, A. SCHWARZEROVÁ, J.
Originální název
Concept Drift Detection in Prediction Classifiers for Determining Gender in Metabolomics Analysis
Typ
článek ve sborníku ve WoS nebo Scopus
Jazyk
angličtina
Originální abstrakt
Currently, one of the most challenges in data analysis is connected to prediction modeling including dynamic information. Metabolomics analysisfocuses 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.
Klíčová slova
Concept drift, Concept drift detection, Metabolomics, Machine learning, Prediction modeling
Autoři
KOŠTOVAL, A.; SCHWARZEROVÁ, J.
Vydáno
26. 4. 2022
Nakladatel
Brno University of Technology, Faculty of Electronic Engineering and Communication
Místo
Brno
ISBN
978-80-214-6029-4
Kniha
Proceedings I of the 28th Conference STUDENT EEICT 2022 General Papers
Edice
1
Strany od
128
Strany do
131
Strany počet
4
URL
https://www.eeict.cz/eeict_download/archiv/sborniky/EEICT_2022_sbornik_1_v2.pdf
BibTex
@inproceedings{BUT179423, author="Aleš {Koštoval} and Jana {Schwarzerová}", title="Concept Drift Detection in Prediction Classifiers for Determining Gender in Metabolomics Analysis", booktitle="Proceedings I of the 28th Conference STUDENT EEICT 2022 General Papers", year="2022", series="1", pages="128--131", publisher="Brno University of Technology, Faculty of Electronic Engineering and Communication", address="Brno", isbn="978-80-214-6029-4", url="https://www.eeict.cz/eeict_download/archiv/sborniky/EEICT_2022_sbornik_1_v2.pdf" }