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. 2019 Jul 15;19(14):3119. doi: 10.3390/s19143119
Algorithm 1 Random Subspace learning classifier.
  • Input:TNS, ZMS, B, ML

  • Output: YlM

  • 1:

    forb=1 to Bdo                                ▹ Training phase

  • 2:

      Set the dimension SF;

  • 3:

      Select randomly s features from TNF to derive TNS;

  • 4:

      CLb=Build_RSL(TNS,ML)▹ Build Random subspace learning classifier based on dataset TNS and machine learning algorithm ML;

  • 5:

    end for

  • 6:

    forb=1 to Bdo                                ▹ Testing phase

  • 7:

      PbMCLb(ZMS)            ▹ Classify ZMS instances using the built model;

  • 8:

    end for

  • 9:

    PlM=MajorityVote({PbM}b=1B)   ▹ Find predicted class labels YM using Majority voting of base classifier models;

  • 10:

    returnPlM;