Bayesian network is one of the most classical and effective models in bigdatagraphalgorithms. Aiming at the problem of learning Bayesian network structure from large-scale datasets, a novel algorithm with the combi...
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ISBN:
(纸本)9781509036776
Bayesian network is one of the most classical and effective models in bigdatagraphalgorithms. Aiming at the problem of learning Bayesian network structure from large-scale datasets, a novel algorithm with the combination of Information theory, Tabu search and Akaike Information Criterion (AIC) called ITA is proposed. Firstly, a dimension reduction algorithm based on information theory is used to filter non-target variables. The variables closely related to the target are picked as the vertexes in Bayesian network. Then choosing AIC as the scoring method and Tabu Search as the heuristic algorithm, a new learning algorithm is adopted to build the global optimal structure. Experimental results demonstrate that ITA algorithm can obtain core causal relationships from largescale datasets in certain area accurately and construct clean and straightforward Bayesian network structure at a lower time cost. Therefore, ITA is an effective and efficient bigdatagraphalgorithm for learning Bayesian network structure from largescale datasets.
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