Incremental Hill-Climbing Search Applied to Bayesian Network Structure Learning
نویسنده
چکیده
We propose two general heuristics to transform a batch Hillclimbing search into an incremental one. Then, we apply our heuristics to two Bayesian network structure learning algorithms and experimentally see that our incremental approach saves a significant amount of computing time while it yields similar networks than the batch algorithms.
منابع مشابه
Improving High-Dimensional Bayesian Network Structure Learning by Exploiting Search Space Information
Bayesian networks are frequently used to model statistical dependencies in data. Without prior knowledge of dependencies in the data, the structure of a Bayesian network is learned from the data. Bayesian network structure learning is commonly posed as an optimization problem where search is used to find structures that maximize a scoring function. Since the structure search space is superexpon...
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