Authors:
T. Schön
1
;
M. Stetter
2
;
A. M. Tomé
3
and
E. W. Lang
4
Affiliations:
1
University of Regensburg and University of Applied Science Weihenstephan-Triesdorf, Germany
;
2
University of Applied Science Weihenstephan-Triesdorf, Germany
;
3
University of Aveiro and University of Regensburg, Portugal
;
4
University of Regensburg, Germany
Keyword(s):
Bayesian Network, Structure Learning, Physarum Solver, LAGD Hill Climber.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Data Manipulation
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Methodologies and Methods
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Physiological Processes and Bio-Signal Modeling, Non-Linear Dynamics
;
Sensor Networks
;
Soft Computing
Abstract:
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.