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Proceedings Paper

An evolutionary algorithmic approach to learning a Bayesian network from complete data
Author(s): Ferat Sahin; Jason Tillet; Raghuveer Rao; T. M. Rao
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Paper Abstract

Discovering relationships between variables is crucial for interpreting data from large databases. Relationships between variables can be modeled using a Bayesian network. The challenge of learning a Bayesian network from a complete dataset grows exponentially with the number of variables in the database and the number of states in each variable. It therefore becomes important to identify promising heuristics for exploring the space of possible networks. This paper utilizes an evolutionary algorithmic approach, Particle Swarm Optimization (PSO) to perform this search. A fundamental problem with a search for a Bayesian network is that of handling cyclic networks, which are not allowed. This paper explores the PSO approach, handling cyclic networks in two different ways. Results of network extraction for the well-studied ALARM network are presented for PSO simulations where cycles are broken heuristically at each step of the optimization and where networks with cycles are allowed to exist as candidate solutions, but are assigned a poor fitness. The results of the two approaches are compared and it is found that allowing cyclic networks to exist in the particle swarm of candidate solutions can dramatically reduce the number of objective function evaluations required to converge to a target fitness value.

Paper Details

Date Published: 12 April 2004
PDF: 12 pages
Proc. SPIE 5433, Data Mining and Knowledge Discovery: Theory, Tools, and Technology VI, (12 April 2004); doi: 10.1117/12.542371
Show Author Affiliations
Ferat Sahin, Rochester Institute of Technology (United States)
Jason Tillet, Rochester Institute of Technology (United States)
Raghuveer Rao, Rochester Institute of Technology (United States)
T. M. Rao, SUNY/Brockport (United States)


Published in SPIE Proceedings Vol. 5433:
Data Mining and Knowledge Discovery: Theory, Tools, and Technology VI
Belur V. Dasarathy, Editor(s)

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