Dear, good night. I'm sorry for the English, I'm using a translator.
I am using MEKA for my master's work, which is using machine learning to predict student retention in higher education, and I came across the following situation. Using the GUI interface and running Meka Explorer
to test the multitarget algorithms, the results of Hamming score and Accuracy (per label) are the same for several different algorithms. I used two multitarget datasets available in the data MEKA folder, the
thyroid-L7.arff and solar_flare.arff, and the same behavior of equal metrics for different algorithms occurs.

Using meka.classifiers.multitarget.CC, meka.classifiers.multitarget.BCC, meka.classifiers.multitarget.CCp and meka.classifiers.multitarget.CR, all running with J48 and NaiveBayes, with default parameters, present the same results as Hamming score, Exact match, Hamming loss, ZeroOne loss, Levenshtein distance and Accuracy (per label).

I ran the experiments on both Mac OSX and Ubuntu.

The result is this for all the algorithms and variations mentioned above, using the thyroid-L7.arff dataset.

N (test) 3119
L 7
Hamming score 0.281
Exact match 0
Hamming loss 0.719
ZeroOne loss 1
Levenshtein distance 0.719
Label indices [0 1 2 3 4 5 6]
Accuracy (per label) [0.002 0.023 0.006 0.939 0.013 0.001 0.980]