  Multi-label classification (MLC) has recently received increasing interest
from the machine learning community. Several studies provide reviews of methods
and datasets for MLC and a few provide empirical comparisons of MLC methods.
However, they are limited in the number of methods and datasets considered.
This work provides a comprehensive empirical study of a wide range of MLC
methods on a plethora of datasets from various domains. More specifically, our
study evaluates 26 methods on 42 benchmark datasets using 20 evaluation
measures. The adopted evaluation methodology adheres to the highest literature
standards for designing and executing large scale, time-budgeted experimental
studies. First, the methods are selected based on their usage by the community,
assuring representation of methods across the MLC taxonomy of methods and
different base learners. Second, the datasets cover a wide range of complexity
and domains of application. The selected evaluation measures assess the
predictive performance and the efficiency of the methods. The results of the
analysis identify RFPCT, RFDTBR, ECCJ48, EBRJ48 and AdaBoostMH as best
performing methods across the spectrum of performance measures. Whenever a new
method is introduced, it should be compared to different subsets of MLC
methods, determined on the basis of the different evaluation criteria.
