Probabilistic Networks and Expert Systems
Probabilistic Networks and Expert Systems
A Hybrid Data Mining Approach To Discover Bayesian Networks Using Evolutionary Programming
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Probalistic Network Construction Using the Minimum Description Length Principle
ECSQARU '93 Proceedings of the European Conference on Symbolic and Quantitative Approaches to Reasoning and Uncertainty
Equivalence and synthesis of causal models
UAI '90 Proceedings of the Sixth Annual Conference on Uncertainty in Artificial Intelligence
An empirical comparison of three inference methods
UAI '88 Proceedings of the Fourth Annual Conference on Uncertainty in Artificial Intelligence
Neural Network Based Systems for Prostate Cancer Stage Prediction
IJCNN '00 Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 3 - Volume 3
Learning equivalence classes of bayesian-network structures
The Journal of Machine Learning Research
Exact Bayesian Structure Discovery in Bayesian Networks
The Journal of Machine Learning Research
Large-Sample Learning of Bayesian Networks is NP-Hard
The Journal of Machine Learning Research
A chain-model genetic algorithm for Bayesian network structure learning
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Building a GA from design principles for learning Bayesian networks
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
Learning Bayesian networks with local structure
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
Learning Bayesian network structures by searching for the best ordering with genetic algorithms
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
A review on evolutionary algorithms in Bayesian network learning and inference tasks
Information Sciences: an International Journal
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In this paper, we report on work done evolving Bayesian Networks with Genetic Algorithms. We use a Chain Model GA [19] to induce a Bayesian network model for the real world problem of Prostate Cancer management. Bayesian networks can and have been used in a wide range of complex domains, notably in medicine. In fact, they have shown powerful capabilities in representing and dealing with the uncertainties generally inherent in the clinical practice. In this study, we investigate those capabilities by testing the evolved model's predictive power and exploring its potential use as a more versatile alternative to the widely used Partin tables for prostate cancer pathology staging.