Automatic knowledge base refinement for classification systems
Artificial Intelligence
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Extensions to the CART algorithm
International Journal of Man-Machine Studies
Algorithmic Program DeBugging
Machine Learning
UAI '89 Proceedings of the Fifth Annual Conference on Uncertainty in Artificial Intelligence
Equivalence and synthesis of causal models
UAI '90 Proceedings of the Sixth Annual Conference on Uncertainty in Artificial Intelligence
Learning causal trees from dependence information
AAAI'90 Proceedings of the eighth National conference on Artificial intelligence - Volume 2
Changing the rules: a comprehensive approach to theory refinement
AAAI'90 Proceedings of the eighth National conference on Artificial intelligence - Volume 2
Refinement of approximate domain theories by knowledge-based neural networks
AAAI'90 Proceedings of the eighth National conference on Artificial intelligence - Volume 2
Learning locally minimax optimal Bayesian networks
International Journal of Approximate Reasoning
Review: learning bayesian networks: Approaches and issues
The Knowledge Engineering Review
Modeling sequences of user actions for statistical goal recognition
User Modeling and User-Adapted Interaction
Artificial Intelligence in Medicine
Using literature and data to learn Bayesian networks as clinical models of ovarian tumors
Artificial Intelligence in Medicine
A review on probabilistic graphical models in evolutionary computation
Journal of Heuristics
ABC-miner: an ant-based bayesian classification algorithm
ANTS'12 Proceedings of the 8th international conference on Swarm Intelligence
Score-based methods for learning Markov boundaries by searching in constrained spaces
Data Mining and Knowledge Discovery
A review on evolutionary algorithms in Bayesian network learning and inference tasks
Information Sciences: an International Journal
Regularized continuous estimation of distribution algorithms
Applied Soft Computing
An ensemble of Bayesian networks for multilabel classification
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Adaptive thresholding in structure learning of a Bayesian network
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Annealed importance sampling for structure learning in Bayesian networks
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Bayesian network modeling of Port State Control inspection findings and ship accident involvement
Expert Systems with Applications: An International Journal
Incremental causal network construction over event streams
Information Sciences: an International Journal
Learning optimal bayesian networks: a shortest path perspective
Journal of Artificial Intelligence Research
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Theory refinement is the task of updating a domain theory in the light of new cases, to be done automatically or with some expert assistance. The problem of theory refinement under uncertainty is reviewed here in the context of Bayesian statistics, a theory of belief revision. The problem is reduced to an incremental learning task as follows: the learning system is initially primed with a partial theory supplied by a domain expert, and thereafter maintains its own internal representation of alternative theories which is able to be interrogated by the domain expert and able to be incrementally refined from data. Algorithms for refinement of Bayesian networks are presented to illustrate what is meant by "partial theory", "alternative theory representation", etc. The algorithms are an incremental variant of batch learning algorithms from the literature so can work well in batch and incremental mode.