MRMD: Maximum-Relevance-Maximum-Distance


Feature selection method has two main part of the decision: The Pearson correlation coefficient shows the closely relationship between features and labels.Distance between features is used to measure the redundancy. Finally, MRMD selects features which has strong correlation with labeled and lowest redundancy features subset Pearson



Or you can use github to download them.

java -jar mrmd.jar -i input -o output.txt

  • -i inputFile
  • -o outputFile
  • Parameters
    the distance function default(1)
    (1 = Euclidean Distance, 2 = Cosine Distance, 3 = Tanimoto coefficient, 4 = mean)
    outputfile of arff default (opt.arff)
    opt model type defauly(rf)
    (rf, svm, bagging)
    (4)-sn 100
    (5)-N (no opt)


    We have got our project on Github(

    All Rights Reserved Copyright @ 2016|Prof. Quan Zou
    Last Modified in 2016/11/2