  We study the performance of data-driven, a priori and random approaches to
label space partitioning for multi-label classification with a Gaussian Naive
Bayes classifier. Experiments were performed on 12 benchmark data sets and
evaluated on 5 established measures of classification quality: micro and macro
averaged F1 score, Subset Accuracy and Hamming loss. Data-driven methods are
significantly better than an average run of the random baseline. In case of F1
scores and Subset Accuracy - data driven approaches were more likely to perform
better than random approaches than otherwise in the worst case. There always
exists a method that performs better than a priori methods in the worst case.
The advantage of data-driven methods against a priori methods with a weak
classifier is lesser than when tree classifiers are used.
