A Novel Approach of Multiple Submodel Integration Based on Decision Forest Construction

  •  Limin Wang    
  •  Xiaolin Li    
  •  Yuting Mao    


An analytical general solution is derived for reasoning uncertain knowledge by multiple sub-model integration. By choosing decision rule for each specific instance, a decision forest rather than a tree will be constructed, thus all relatively independent attribute sets can be determined automatically without any human intervention. Necessary discretization for mixed-mode subset will be processed based on post-discretization strategy to minimize information loss.

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