MRMD

MRMD: Maximum-Relevance-Maximum-Distance

MRMD

Feature selection method has two main part of the decision:

Pearson's correlation coefficient (PCC) is utilized to measure the relevance between features in a subset
Euclidean distance (ED),Cosine distance (CD) and Tanimoto (TO) is utilized to calculate the redundancy among features in a subset

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

Usage

mrmd.jar

Or you can use github to download them.

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

-i inputFile

-o outputFile

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

Download

We have got our project on Github(https://github.com/heshida01/mrmd)