Mathematical Problems in Engineering / 2021 / Article / Alg 2

Research Article

Boosted Fuzzy Granular Regression Trees

Algorithm 2

Fuzzy granular regression tree.
 Input: instance set , regression value set
 Output: fuzzy granular tree
(1) Remove the instances missing some attribute values.
(2) Normalize each attribute value.
(3) Calculate the cluster center set (Algorithm 1).
(4) and . // Parallel distributed fuzzy granulation.
(5)//This is parallel process. Here, take as example.
  FOR to
   FOR to
   , sample is fuzzy granulated as
   Build a fuzzy granular vector ;
   Get label of , ;
   A fuzzy granular rule can be built. ;
(6) Select the optimal segmentation variable (i.e., the attribute ) and segmentation point (i.e., ) by solving equation
  That is, traverse variable to find the pair that minimizes the loss function by fixing the segmentation variable and scanning segmentation point .
(7)Divide the area with the selected pair and decide output value as follows:
(8) Continue to call Step 6 and Step 7 for the two subregions until the number of split nodes is .
(9) Divide the input fuzzy granular space into regions and generate a fuzzy granular regression tree

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