IEEE Transactions on Pattern Analysis and Machine Intelligence
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Using Bayesian networks to analyze expression data
RECOMB '00 Proceedings of the fourth annual international conference on Computational molecular biology
Data mining of Bayesian networks using cooperative coevolution
Decision Support Systems
MicroGP—An Evolutionary Assembly Program Generator
Genetic Programming and Evolvable Machines
Modeling Human Expertise on a Cheese Ripening Industrial Process Using GP
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
Bayesian network structure learning using cooperative coevolution
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Journal of Electronic Testing: Theory and Applications
Evolutionary Optimization: the GP toolkit
Evolutionary Optimization: the GP toolkit
A cooperative coevolutionary genetic algorithm for learning bayesian network structures
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Future Generation Computer Systems
Hi-index | 0.00 |
Bayesian networks are stochastic models, widely adopted to encode knowledge in several fields. One of the most interesting features of a Bayesian network is the possibility of learning its structure from a set of data, and subsequently use the resulting model to perform new predictions. Structure learning for such models is a NP-hard problem, for which the scientific community developed two main approaches: score-and-search metaheuristics, often evolutionary-based, and dependency-analysis deterministic algorithms, based on stochastic tests. State-of-the-art solutions have been presented in both domains, but all methodologies start from the assumption of having access to large sets of learning data available, often numbering thousands of samples. This is not the case for many real-world applications, especially in the food processing and research industry. This paper proposes an evolutionary approach to the Bayesian structure learning problem, specifically tailored for learning sets of limited size. Falling in the category of score-and-search techniques, the methodology exploits an evolutionary algorithm able to work directly on graph structures, previously used for assembly language generation, and a scoring function based on the Akaike Information Criterion, a well-studied metric of stochastic model performance. Experimental results show that the approach is able to outperform a state-of-the-art dependency-analysis algorithm, providing better models for small datasets.