A New Physarum Learner for Network Structure Learning from Biomedical Data
- A novel structure learning algorithm for Bayesian Networks based on a Physarum Learner is presented. The length of the connections within an initially fully connected Physarum-Maze is taken as the inverse Pearson correlation coefficient between the connected nodes. The Physarum Learner then estimates the shortest indirect paths between each pair of nodes. In each iteration, a score of the surviving edges is incremented. Finally, the highest scored connections are combined to form a Bayesian Network. The novel Physarum Learner method is evaluated with different configurations and compared to the LAGD Hill Climber showing comparable performance with respect to quality of training results and increased time efficiency for large data sets.
Author: | Torsten SchönORCiD, Martin Stetter, Ana Maria Tomé, Elmar W. Lang |
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Language: | English |
Document Type: | Conference Paper |
Conference: | BIOSIGNALS 2013 - International Conference on Bio-inspired Systems and Signal Processing, Barcelona (Spain), 11.-14.02.2013 |
Year of first Publication: | 2013 |
published in (English): | Proceedings of the International Conference on Bio-inspired Systems and Signal Processing BIOSTEC |
Editor(s): | Sergio A. Alvarez, Jordi Solé-Casals, Ana Fred, Hugo Gamboa |
Publisher: | SciTePress |
Place of publication: | Setúbal |
ISBN: | 978-989-8565-36-5 |
First Page: | 151 |
Last Page: | 156 |
Review: | peer-review |
Open Access: | ja |
URN: | urn:nbn:de:bvb:573-48033 |
Related Identifier: | https://doi.org/10.5220/0004227401510156 |
Licence (German): | ![]() |
Release Date: | 2024/07/01 |