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Concept drift in metabolomic analysis
Koštoval, Aleš ; Provazník, Ivo (referee) ; Schwarzerová, Jana (advisor)
This bachelor thesis deals with machine learning, specifically the analysis of the concept drift. This is an unwanted phenomenon that can be detected in predictive models. Through detection followed by correction of the concept drift, predictive models become more reliable and can respond adequately to input data representing dynamic information. Metabolomic data can be considered a suitable representative of such data. Metabolomic data and their analysis can help to detect diseases such as diabetes mellitus or cancer early. In the first part of this bachelor thesis, the theoretical background of concept drift analysis and metabolomics analysis are described. The second part discusses the process of modeling predictive classifiers and implementing algorithms for concept drift detection. The practical part of the work was implemented in the Python programming language. Finally, the second part describes the results obtained and their discussion.
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Correction of the concept of drift in prediction models
Michálková, Eva ; Provazník, Valentine (referee) ; Schwarzerová, Jana (advisor)
The main goal of this bachelor thesis is the analysis of concept drift in metabolomics. Concept drift is an undesirable phenomenon and can be caused by nonstationary data. It can have a negative impact on the performance and reliability of predictive modelling. This challenge can be solved by concept drift detection and subsequent correction. One of the fields where this issue has recently emerged is metabolomic diagnostics. Metabolomic data analysis can lead to early detection of several serious diseases, which can help with the recovery process. When diagnosing an illnes predictive models present a way to make the process more efficient, faster and give the option of personalization. The first part of this thesis specifies concept drift, it’s detection and correction methods and the importance of metabolomics and prediction models. The second part deals with the implementation of some available algorithms for concept drift detection and correction and the implementation of automatic concept drift correction. Finally, in the second part results and their discussion are described.
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Concept drift in metabolomic analysis
Koštoval, Aleš ; Provazník, Ivo (referee) ; Schwarzerová, Jana (advisor)
This bachelor thesis deals with machine learning, specifically the analysis of the concept drift. This is an unwanted phenomenon that can be detected in predictive models. Through detection followed by correction of the concept drift, predictive models become more reliable and can respond adequately to input data representing dynamic information. Metabolomic data can be considered a suitable representative of such data. Metabolomic data and their analysis can help to detect diseases such as diabetes mellitus or cancer early. In the first part of this bachelor thesis, the theoretical background of concept drift analysis and metabolomics analysis are described. The second part discusses the process of modeling predictive classifiers and implementing algorithms for concept drift detection. The practical part of the work was implemented in the Python programming language. Finally, the second part describes the results obtained and their discussion.
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