  In recent years, multi-label classification problem has become a
controversial issue. In this kind of classification, each sample is associated
with a set of class labels. Ensemble approaches are supervised learning
algorithms in which an operator takes a number of learning algorithms, namely
base-level algorithms and combines their outcomes to make an estimation. The
simplest form of ensemble learning is to train the base-level algorithms on
random subsets of data and then let them vote for the most popular
classifications or average the predictions of the base-level algorithms. In
this study, an ensemble learning method is proposed for improving multi-label
classification evaluation criteria. We have compared our method with well-known
base-level algorithms on some data sets. Experiment results show the proposed
approach outperforms the base well-known classifiers for the multi-label
classification problem.
