Work

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

Year: 2020

Type: article

Abstract: 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.

Source: Entropy

DOI: 10.3390/e22121436

Authors: Hanna Loch-Olszewska, Janusz Szwabiński

Institution: Wrocław University of Science and Technology

Language: English

Topic: Fractional Differential Equations Solutions

Subfield: Modeling and Simulation

Field: Mathematics

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $1,690

Funder: Narodowe Centrum Nauki