This is a MATLAB package for MLDA classification.


     pr_feature_select     Two-sample two-tailed t-tests for feature selection

               pr_MLDA     MLDA classification algorithm.

          pr_test_MLDA     Computation the performance of classifier.

pr_classification_MLDA     MLDA classifier using feature selection.

  pr_classification_M3     M3 method. 



Note that the program requires setting the inputdata in a matrix of n*m and the label of patient group is -1, the label of nomal cotrol is 1. n is the number of subjects and m is the number of features. m=number of the regions of interest (ROIs)*the number of property of feature. We put the features of each property together.

If you only have one type of features or you want to concatenated the different types of features into a longer feature vector, you need to use pr_classification_MLDA function to obtain the classifier and the performance of the classifier.

If you want to combine the different types of features through weighted voting, you need to use pr_classification_M3 to obtain the classifier and the performance of the classifier.

Note: each type of features should have the same number.

Ackonwledgement£ºThis work was supported by the Natural Science Foundation of China (Grant Nos. 81030028 and 30870667) and Beijing Natural Science Foundation (Grant No. 7102090).
