Publication detail

Concept Drift Detection in Prediction Classifiers for Determining Gender in Metabolomics Analysis

KOŠTOVAL, A. SCHWARZEROVÁ, J.

Original Title

Concept Drift Detection in Prediction Classifiers for Determining Gender in Metabolomics Analysis

Type

conference paper

Language

English

Original Abstract

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.

Keywords

Concept drift, Concept drift detection, Metabolomics, Machine learning, Prediction modeling

Authors

KOŠTOVAL, A.; SCHWARZEROVÁ, J.

Released

26. 4. 2022

Publisher

Brno University of Technology, Faculty of Electronic Engineering and Communication

Location

Brno

ISBN

978-80-214-6029-4

Book

Proceedings I of the 28th Conference STUDENT EEICT 2022 General Papers

Edition

1

Pages from

128

Pages to

131

Pages count

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"
}