Theory refinement on Bayesian networks
Proceedings of the seventh conference (1991) on Uncertainty in artificial intelligence
Bayesian Network Refinement Via Machine Learning Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
Time and sample efficient discovery of Markov blankets and direct causal relations
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Thesis: incremental methods for Bayesian network structure learning
AI Communications
The Journal of Machine Learning Research
Adapting Bayes network structures to non-stationary domains
International Journal of Approximate Reasoning
A Novel Scalable and Data Efficient Feature Subset Selection Algorithm
ECML PKDD '08 Proceedings of the European conference on Machine Learning and Knowledge Discovery in Databases - Part II
Adaptive Bayesian network classifiers
Intelligent Data Analysis
Knowledge Discovery from Data Streams
Knowledge Discovery from Data Streams
Sequential update of Bayesian network structure
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
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The dynamic nature of data streams leads to a number of computational and mining challenges. In such environments, Bayesian network structure learning incrementally by revising existing structure could be an efficient way to save time and memory constraints. The local search methods for structure learning outperforms to deal with high dimensional domains. The major task in local search methods is to identify the local structure around the target variable i.e. parent children (PC). In this paper we transformed the local structure identification part of MMHC algorithm into an incremental fashion by using heuristics proposed by reducing the search space. We applied incremental hill-climbing to learn a set of candidate- parent-children (CPC) for a target variable. Experimental results and theoretical justification that demonstrate the feasibility of our approach are presented.