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    基于贝叶斯网的决策表系统的优化分解

    Optimal Decomposition of Decision Table Systems Based on Bayesian Networks

    • 摘要: 提出了决策表系统的基于广义决策函数(GDF)与基于贝叶斯网的分解方法是等价的;指出决策表系统的分解问题可归结为求解与决策表系统相应的多模块贝叶斯网(MSBN)及其d-割集;对同一个贝叶斯网(BN)具有不同的d-割集,存在不同的分解模式,提出并证明了MSBN的d-割集和连接联合森林(LJF)的割集之间的关系,而且LJF的割集决定着MSBN优化的d-割集,这样决策表系统分解问题也就是求解LJF的割集;最后通过案例说明提出的方法的可行性.

       

      Abstract: It is shown that the decomposition method based on GDF (generalized decision function) is equivalent to that based on Bayesian networks in decision table systems; It is pointed out that the problem of information system decomposition is boiled down to those solving multiple sectioned Bayesian network (MSBN) and its d-separator set (d-sepset) corresponding to decision table systems; For the same Bayesian network (BN) owning various d-sepsets, various decomposition models exist. The relation between d-sepsets of MSBN and separator sets (sepsets) of linked junction forest (LJF) are put forward and proven, and it is shown that sepsets of LJF decide optimal d-sepsets of MSBN. Therefore the problem of decomposition of decision table systems is also to solve sepsets of LJF. Finally, feasibility of the method put forward is verified through an example.

       

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