A new physarum learner for network structure learning from biomedical data

• Autor(es): Schön, T. ; Stetter, M. ; Tomé, A. M. ; Lang, E. W.
• Data: 2013
• Identificador Persistente: http://hdl.handle.net/10773/10912
• Origem: RIA - Repositório Institucional da Universidade de Aveiro
• Assunto(s): Bayesian Network; Structure Learning; Physarum Solver; LAGD Hill Climber
• Tipo de Documento: Documento de conferência
• Idioma: Inglês

Descrição

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.