Random Subspace Aggregation for Cancer Prediction with Gene Expression Profiles

Algorithm 1

Input:

Dataset , sample size ;

Sample , number of total feature ;

Class of th sample in normal, tumor};

Split function: yield training set and testing set from original dataset. If the original

dataset has been divided into training and testing partition, this step could be skipped.

Gene select function: , where is the feature number of selected data, ;

RS_preject function: , where is the size of a random subspace, ;

 Number of random subspaces ;

Learning algorithm: SVM

Output:

Classification hypotheses :

Start:

Data processing:

  (Trainset, Testset) = Split()

  TrainsetNew = Gene_select(Trainset, )

  TestsetNew = Gene_select(Testset, )

Generate and aggregate SVM classifiers on random subspaces:

  For to

    _project(TrainsetNew, )

    ()

  End

Test:

  For each in TestsetNew

  End

End