Hongliang Yao

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The current algorithms of learning the structure of dynamic Bayesian networks attempt to find single "best" model. However, this approach ignores the uncertainty in model selection and is prone to overfitting and local optimal problem. Markov chain Monte Carlo algorithm based on Bayesian model averaging can provide a way for accounting for this model(More)
The search for an optimal node elimination sequence for the triangulation of Bayesian networks is an NP-hard problem. In this paper, a new method, called the TAGA algorithm, is proposed to search for the optimal node elimination sequence. TAGA adjusts the probabilities of crossover and mutation operators by itself, and provides an adaptive ranking-based(More)
The deficiencies of keeping population diversity, prematurity and low success rate of searching the global optimal solution are the shortcomings of genetic algorithm (GA). Based on the bias of samples in the uniform design sampling (UDS) point set, the crossover operation in GA is redesigned. Using the concentrations of antibodies in artificial immune(More)
Associative classifiers have received considerable attention due to their easy to understand models and promising performance. However, with a high dimensional dataset, associative classifiers inevitably face two challenges: (1) how to extract a minimal set of strong predictive rules from an explosive number of generated association rules, and (2) how to(More)
As intervened edges are difficult to be determined when intervention method is used for learning the causal relationships of probability model, an active learning method (Structural Intervention Learning of Sensitivity Analysis –SILSA Algorithm) is proposed. SILSA algorithm obtains original network structure based on k2 algorithm, then uses junction tree(More)
Latent variables often play an important role in improving the quality of the learned Bayesian networks and understanding the nature of interactions in the model. The dimensionality of latent variables has significant effect on the representation quality and complexity of the model. The maximum possible dimensionality of a latent variable is a Cartesian(More)
We introduce a dynamic influence diagram (DID) to model a multi-agent system in dynamic environment, where the DID is an extension of a static influence diagram over time. Based on splitting junction tree and Boyen-Koller algorithm, an extensional junction tree approximate inference algorithm of DID is proposed in this paper, where clusters of junction tree(More)
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