Mathematical Problems in Engineering / 2021 / Article / Alg 3

Research Article

Boosted Fuzzy Granular Regression Trees

Algorithm 3

Boosted fuzzy granular regression trees.
 Input: instance set , regression value set , the number of fuzzy granular regression tree
 Output: boosted fuzzy granular regression trees
(1) Get a fuzzy granular vector rule base by parallel fuzzy granulation of the dataset (see Algorithm 2, Algorithm 4, and Algorithm 5.)
(2) Create tasks, namely,
(3) Execute the following operations for each independent task ():
   MapFunction(key, value), where key = offset of instance and indicates that fuzzy granular vectors are randomly
     //selected from .
     //Randomly select attributes from the attribute set (constitutes attribute subset , that is, )
     //Form a fuzzy granular rule set , build a fuzzy granular regression tree , and get its RMSE
     , where .
(4)   FOR to instances-total-number
(5)    SubsetID = i mod J
(6)    context.write(SubsetID, FuzzyGranularVector)
(7)   END FOR
   END MapFunction
(8)  ReduceFunction(key, value)//Here, key = SubsetID, value = FuzzyGranualrVector
(9)   Job.addCache(FuzzyGranularVector[SubsetID])
(10)    = train(SubsetID, FuzzyGranularVector)//(See Step 6–Step 9 of Algorithm 2.)
(11)   context.write(1,())
   END ReduceFunction
(12)//Calculate BFGRT composed of the linear combination of fuzzy granular regression trees.
  Where .

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