Hongliang Yao

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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)
The search for an optimal node elimination sequence for the triangu-lation 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)
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)
BACKGROUND Gastric cancer is the second most common cause of global cancer-related mortality. Although dedifferentiation predicts poor prognosis in gastric cancer, the molecular mechanism underlying dedifferentiation, which could provide fundamental insights into tumor development and progression, has yet to be elucidated. Furthermore, the molecular(More)
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)
Many types of KIT mutations have been observed in gastrointestinal stromal tumors (GISTs), but their prognostic and predictive significance are still unclear. A meta-analysis and literature review were conducted to estimate the contribution of KIT mutations in prognostic parameters and clinic-pathological significance of GISTs. A total of 18 relevant(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)