Ensemble Merit Merge Feature Selection for Enhanced Multinomial Classification in Alzheimer’s Dementia

Algorithm 1

Algorithm CPEMM (DS, threshold)

Input: DS → dataset, merit_threshold meritvalue,

   acc_threshold accuracy threshold, flag = 1

Output: ffs finalised feature set

   Accuracy vector, MS merged set vector,

bs bootstrap vector

   merit subsets obtained from wrapper feature selection,

   Sorted subset list in descending order

   subset with highest ranking,

   no of subsets generated ()

(1) Initialize subset = null

(2) Generate feature subsets with Ensemble

 Initialise no. of iterations

Repeat

   Generate subset from DS

   Verify merit

   Optimization of feature subset

      merit of

Until iterations

(3) Evaluate accuracy of classifier with subset .

 , , 

 Evaluate accuracy of

 Repeat

  Repeat

   diff =

        and merged

      Evaluate accuracy of

   If

        Append to ffs

        flag = 1

   else

        flag = 0

        Append to ffs

   endif

   Increment

  until flag != 0 or or diff threshold

  Increment

 until ≥ acc_threshold

   (4)    bootstrap vector from ffs

   Repeat

     Train classifier c_i with

     Evaluate out of bag error

   Until sets are bootstrapped

     with 5-fold cross validation