Impact of Feature Choice on Machine Learning Classification of Fractional Anomalous Diffusion

The growing interest in machine learning methods has raised the need for a careful study of their application to the experimental single-particle tracking data. In this paper, we present the differences in the classification of the fractional anomalous diffusion trajectories that arise from the sele...

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Veröffentlicht in:	Entropy (Basel, Switzerland) Jg. 22; H. 12; S. 1436
Hauptverfasser:	Loch-Olszewska, Hanna, Szwabiński, Janusz
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Switzerland MDPI 19.12.2020 MDPI AG
Schlagworte:	anomalous diffusion feature engineering machine learning classification feature engineering machine learning classification anomalous diffusion
ISSN:	1099-4300, 1099-4300
Online-Zugang:	Volltext
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Zusammenfassung:	The growing interest in machine learning methods has raised the need for a careful study of their application to the experimental single-particle tracking data. In this paper, we present the differences in the classification of the fractional anomalous diffusion trajectories that arise from the selection of the features used in random forest and gradient boosting algorithms. Comparing two recently used sets of human-engineered attributes with a new one, which was tailor-made for the problem, we show the importance of a thoughtful choice of the features and parameters. We also analyse the influence of alterations of synthetic training data set on the classification results. The trained classifiers are tested on real trajectories of G proteins and their receptors on a plasma membrane.
Bibliographie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 These authors contributed equally to this work.
ISSN:	1099-4300 1099-4300
DOI:	10.3390/e22121436