Schoen, T. and Stetter, M. and Tome, A. M. and Puntonet, C. G. and Lang, E. W. (2014) Physarum Learner: A bio-inspired way of learning structure from data. EXPERT SYSTEMS WITH APPLICATIONS, 41 (11). pp. 5353-5370. ISSN 0957-4174, 1873-6793
Full text not available from this repository. (Request a copy)Abstract
A novel Score-based Physarum Learner algorithm for learning Bayesian Network structure from data is introduced and shown to outperform common score based structure learning algorithms for some benchmark data sets. The Score-based Physarum Learner first initializes a fully connected Physarum-Maze with random conductances. In each Physarum Solver iteration, the source and sink nodes are changed randomly, and the conductances are updated. Connections exceeding a predefined conductance threshold are considered as Bayesian Network edges, and the score of the connected nodes are examined in both directions. A positive or negative feedback is given to the edge conductance based on the calculated scores. Due to randomness in selecting connections for evaluation, an ensemble of Score-based Physarum Learner is used to build the final Bayesian Network structure. (c) 2014 Elsevier Ltd. All rights reserved.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | ADAPTIVE TRANSPORT NETWORK; SLIME-MOLD; SHORTEST; ALGORITHM; ROBUST; MODEL; Physarum Learner; Structure learning; Bayesian Network |
| Subjects: | 500 Science > 570 Life sciences |
| Divisions: | Biology, Preclinical Medicine > Institut für Biophysik und physikalische Biochemie > Prof. Dr. Elmar Lang |
| Depositing User: | Dr. Gernot Deinzer |
| Date Deposited: | 29 Aug 2019 13:52 |
| Last Modified: | 29 Aug 2019 13:52 |
| URI: | https://pred.uni-regensburg.de/id/eprint/9718 |
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