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

Entropy

Janusz Szwabiński¹  Hanna Loch-Olszewska¹ 

[1] Faculty of Pure and Applied Mathematics, Hugo Steinhaus Center, Wrocław University of Science and Technology, 50-370 Wrocław, Poland;

关键词: anomalous diffusion;    machine learning classification;    feature engineering;   

DOI  :  10.3390/e22121436

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown